Reveal has been a weekly investigative podcast for nearly 10 years now, so we've produced hundreds of hours of investigative journalism over the years designed to inspire, inform, or infuriate you (and occasionally, all three at the same time). We've curated some of our favorite Reveal series and serials to take you through your holiday travel time--episodes that will resonate today and into 2025. You can find the link to each episode on your preferred podcast platform below. Mississippi Goddam (seven-part series): Billey Joe Johnson Jr. dreamed of graduating high school, going to college, and one day playing pro football. On a cold December morning in 2008, that future was shattered.

Manufacturing execution systems (MES) have undergone many transformations in the past several years--from simple point solutions to comprehensive shop floor systems that are now mission-critical to manufacturing operations. As I mentioned in part one of this series, the union between MES and smart manufacturing technology gives manufacturing enterprises access to new, advanced capabilities. From increased operating margins to decreased costs, manufacturers can leverage this smart combination and find themselves with a significant competitive edge globally. In part one and part two of this three-part series, we looked at four significant new aspects of MES: mobility and the use of artificial intelligence (AI), track-and-trace database capabilities and the use of many applications. The IIoT is becoming synonymous with smart manufacturing.

Earlier this month @huggingface released a number of notebooks that walk users through some NLP basics. The three-part series, written by @MorganFunto, covers tokenizers, transformers, and pipelines utilizing Hugging Face's transformer library. The notebooks cover the basics on a high level and get you working in the code quickly. The notebooks written in Colab allows anyone to run the code in the browser. Before going deep into any Machine Learning or Deep Learning Natural Language Processing models, every practitioner should find a way to map raw input strings to a representation understandable by a trainable model.

Yet there's also no point in accessing richer sources of data unless you have an architecture that can consume it. An AI-ready architecture is able to address different shapes and granularities of data such as transactions, logs, geospatial information, sensors and social. In addition, real-time time-series data is key to the constant feed of input that propels data-driven devices, from smart-home appliances and health devices to self-driving cars. Make sure your AI architecture has the capability to consume different data structures in different time dimensions, especially real time. Is your organization identifying and classifying data at the point of ingestion?

Determining whether your data is up to snuff for AI often requires a new set of metrics. For example, key performance indicators (KPIs) for IT regularly include whether the system is ready when reports need to run, or whether a particular report or data feed was executed within the agreed upon time window. For AI to be successful, however, data metrics need to measure business value and the ability to deliver the desired outcomes. That measure doesn't exist today. Measuring your data IQ will help you hone in on business value.

Finding the answers in AI begins with a strategy for a new data foundation. Making sure the foundation has the computing, storage and analytical capabilities engineered for purpose is only part of the equation. Perhaps more important is the shift in perspective that's needed: Instead of using data to track and measure how business functions perform, the new data foundation lets your organization perform with data. It's at this point in conversations about data that ride-sharing giant Uber typically comes up. And with good reason: Everything Uber does is all about its data.

New technologies have allowed for increased and varied avenues of communication between organizations and customers. This has led to improved transparency, more direct support and outreach, and more personal interactions. From a compliance standpoint, however, it has added immense complexity to how organizations navigate regulatory requirements and compliance. In parts one and two of this three-part series, we discussed the internal tension between business and compliance teams when it comes to implementing new communication technologies and how organizations can mitigate the risks that come from such implementations. In this blog post, we'll examine why Smart RegTech is a necessity, discuss common trepidations, and examine how to identify the right technology solution for your organization. If you didn't have a chance to read the first to parts in this three-part series, you may find them here: New communications using produced video, video collaboration, and video over social is expanding at a rapid pace and compliance issues are typically reviewed manually – if they are reviewed at all.

This is the second in a three-part series reporting from Toronto's booming Artificial Intelligence sector where new technologies are being pioneered that will permanently change all of our lives Just 45 seconds in the company of scientist Frank Rudzicz and his machines is all it takes to determine whether or not you are suffering from Alzheimer's disease. In that time, the complex Artificial Intelligence (AI) algorithms that the 37-year-old and his team have developed are able to pick apart your voice and predict the severity of the disease to an accuracy of around 82 per cent (and rising). First, there is your actual use of language. Alzheimer's sufferers tend to leave longer pauses between words, prefer pronouns to nouns (for example, saying "she" rather than a person's name) and give more simplistic descriptions, such as a "car" rather than the model or make. Then there is what Rudzicz calls the "jittter and shimmer" of speech; variations in frequency and amplitude.

Here, she focuses on how AI can provide real value for end users. In the first article in this three-part series, I outlined reasons why designing artificial intelligence (AI) for humans is a strategic imperative. In this second article, we'll discuss how to create AI experiences that go beyond technology buzz and provide real value for end users. I've identified eight strategic pillars to serve as a consumer focused foundation for any ideation, design and development process. We'll begin with the first four of these pillars: The more we emphasize the end user of a potential AI experience, the more likely we are to create something that truly adds value to both our businesses and our lives.