As we rely more on natural language processing to help us navigate our world, it's more important than ever that these artificial intelligence models -- used increasingly in applications such as caption generation for the visually impaired -- remain true to reality. "The issue is that deep learning-based neural language generation models have no guarantees in generating factually correct sentences that are faithful to the input data," said UC Santa Barbara computer scientist William Wang. Over the many iterations it takes for a language generation model to learn how to describe or predict what a scene depicts, elements can creep in, causing phenomena such as errors in data-to-text translations or object hallucinations, in which the caption contains an object or an action that doesn't exist in the image. As a result, unless you have a way of reining in these errors (or you're surrealist painter René Magritte) these mismatches could spell the end of the usefulness of the language generation model being used. "This is a huge problem," said Wang. "Imagine you are using a news summarization system to read earnings reports -- the loss of faithfulness can give you wrong numbers, wrong facts and misinformation. Similarly, if a visually impaired person relies on an image captioning system to see the environment, wrong generation could create serious consequences."

Apple chief executive Tim Cook has addressed rumours that his company is building a car in a new interview. While he declined to confirm any real details of what Apple is planning to release – if anything – he did give an indication of what the company might look to do if it does release a car, as rumoured. He noted that "an autonomous car is a robot" and that Apple looks to integrate hardware and software in all of its products. But the company "investigate so many things internally", many of which never actually "see the light of day", he told Kara Swisher in an interview for her New York Times podcast, Sway. In the same intervew, Mr Cook also discussed his commitment to free speech, his hope that controversial social media app Parler could return to the App Store, and Apple's ongoing fight with competitors including Facebook.

Which economic sectors are likely to benefit the most from the introduction of AI-based Systems, and how is their introduction going to affect us? The introduction of AI-based systems will for sure have effects on virtually any economic sector – in some cases the effects will be tremendous. In fact, AI-based systems are already transforming several industries today, as we speak. Look at the automotive industry and the on-going shift to semi- or even fully autonomous cars. Some colleagues at KIT are doing genuinely groundbreaking research in this area.

The dream of building machines that can do science has inspired scientists for decades. Remarkable advances have been made recently; however, we are still far from achieving this goal. In this paper, we focus on the scientific discovery process where a high level of reasoning and remarkable problem-solving ability are required. We review different machine learning techniques used in scientific discovery with their limitations. We survey and discuss the main principles driving the scientific discovery process. These principles are used in different fields and by different scientists to solve problems and discover new knowledge. We provide many examples of the use of these principles in different fields such as physics, mathematics, and biology. We also review AI systems that attempt to implement some of these principles. We argue that building science discovery machines should be guided by these principles as an alternative to the dominant approach of current AI systems that focuses on narrow objectives. Building machines that fully incorporate these principles in an automated way might open the doors for many advancements.

Kustomer announces today that it was named a winner in the Business Intelligences' Artificial Intelligence Excellence Awards program. The company's top-rated customer service CRM platform leverages AI extensively to help industry-leading businesses orchestrate unified, on-demand experiences that create customers for life. "Customer service organizations played a pivotal role during the pandemic as they became a lifeline for customers dealing with uncertainty. Our AI-powered platform also became a lifeline for businesses, helping them keep up with customer concerns and prevent issues before they arose," said Brad Birnbaum, founder and CEO, Kustomer. "I'm incredibly proud that our team is being recognized by Business Intelligence Group for our innovation and ability to deliver tools that our customers need."

Amir Hayeri, CEO of Bio Conscious Tech, works with chronically ill patients to help them predict and ideally avoid disease complications. When you hear the word "glucose," what do you think of? For most people, the next word they think of is "diabetes." More than 10% of the U.S. population is diagnosed with diabetes; so is more than 8% of the Canadian population. An even larger population is pre-diabetic.

The AI Investor recently caught up with Jeremiah Lowin, founder of Prefect, an exciting AI startup with offices in Washington, DC and San Francisco. Jeremiah has a Finance/Risk Management background. The company is setting the standard in dataflow automation used to build, run, and monitor millions of data workflows and pipelines. While his father is a value investor and entrepreneur, Jeremiah likes to dabble in side projects that catch his interest, but having started a business a decade ago, being a founder again wasn't something he was looking for. Constantly experimenting, Jeremiah discovered people wanted to pay him for what he was building.

Lilt, the modern language service and technology provider, today announced it was named a winner in the Business Intelligence Group's Artificial Intelligence Excellence Awards program. Lilt's localization solution combines a community of the world's best professional translators with its AI-powered translation platform, bringing human-powered, technology-assisted translations to global enterprises like Intel, ASICS, Canva, DigitalOcean, WalkMe, and others. "We're thrilled to be recognized as a winner of the Artificial Intelligence Excellence Awards," said Spence Green, CEO of Lilt. "As a language service and technology provider, our AI and machine learning platform enables our customers to provide their customers with a consistent global experience, regardless of what language they speak." Lilt provides businesses with the ability to offer the same global experience to all customers, partners, and employees irrespective of language.

Managing Riskified's Data Science department entails a lot of recruiting -- we've more than doubled in less than a year-and-a-half. As the hiring manager for several of the positions, I also read through a lot of CVs. Recruiters screen through a CV in 7.4 seconds, and after recruiting for several years my average time is pretty fast, but not that extreme. In this blog, I'm going to walk you through my personal heuristics ('cheats') that help me screen a resume. While I can't guarantee that others use the same heuristics, and different roles will differ in the importance of each point, paying attention to these points can help you conquer the CV screen stage.

I've been keeping an eye on the use of machine learning algorithms, particularly by venture capitalists, to make investment decisions for some time now. They've been investing in machine learning companies for years, so applying their products to other businesses, once you have studied how they work, seems a reasonable proposition. After all, what is the decision to invest in a startup based on? Basically, the fruit of a set of analyses and previous experiences that can be systematized and verified in different ways, while the experience corresponds, in reality, to the imperfect distillation, with its biases and errors, of a series of previous decisions, weighted by the results obtained in each. That said, venture capitalists are not entirely objective: they usually allow multiple factors to enter the decision-making process, which include anything from the feelings generated by the company's founding team, to more or less rigorous analyses of its capacity for future development.