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.

As Star Trek star William Shatner turns 90, he's signing up as a brand ambassador for StoryFile, a company focused on artificial intelligence. Shatner will become the first person to use a new program called StoryFile Life to create "an AI-powered interactive conversational video so family and friends can interact with him for years to come." Shatner's StoryFile profile, which uses AI and the company's proprietary technology Conversa, will be made available for the public to interact with on internet-connected devices in May. "This is for all my children and all my children's children and all my children's loved ones and all the loved ones of the loved ones," Shatner said in a press release. You can watch a behind-the-scenes video of the recording here.

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.