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.

Sticks and stones may break my bones, but words will never hurt me. This was a mantra I picked up on the playground at elementary school--something I repeated over and over again anytime I came face to face with racism. It was a coping mechanism meant to guard my heart from the cacophony of discriminatory comments that shaped me as a young Korean American girl growing up in predominantly white spaces. But now that I'm well into adulthood, I think about the girls of color who are also being taught to pretend that words don't hurt--and the people this way of thinking actually protects. It's hard to escape the unrelenting consequences of racism: In the past year alone, we lost Breonna Taylor, George Floyd, Ahmaud Arbery, and the six women of Asian descent murdered in Atlanta (Xiaojie "Emily" Tan, Daoyou Feng, Suncha Kim, Yong Ae Yue, Soon Chung Park, Hyun Jung Grant) at the hands of this insidious disease--and those are just the names that were in the headlines. If we don't acknowledge ...

Back in the 1960s, Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory created one of the first chatbots, which he named ELIZA. Weizenbaum modeled its conversational style after Carl Rogers, a psychotherapist who was known for parroting patients' responses back at them. His hypothesis was that while chatbots could emulate human conversation superficially, they could not fully capture the nuances of a genuine discussion between humans. To Weizenbaum's complete surprise, ELIZA did end up fooling many people into believing they were having a therapeutic breakthrough with a real live therapist. While modern customer service bots aren't likely to help customers dig deep into their psychological issues, the technology behind computers processing human language is getting more advanced day by day.

I know this has been asked many times and in many different ways. And there are tons of blog posts, articles, videos and courses addressing this and comparing hundreds of tools, libraries, frameworks… And that's part of my problem: I am facing so many options that I feel like Buridan's ass, dying of starvation for not knowing what to do. Although I don't want to write too much, I need to speak a little about our situation, in order to put the question in our context. We have only four people, which could be qualified as beginner data scientists. One of us has a profile that is a little bit more "engineer", so data engineer could be more suitable for him.

What's New: Artificial intelligence (AI) developer Jagadish K. Mahendran and his team designed an AI-powered, voice-activated backpack that can help the visually impaired navigate and perceive the world around them. The backpack helps detect common challenges such as traffic signs, hanging obstacles, crosswalks, moving objects and changing elevations, all while running on a low-power, interactive device. "Last year when I met up with a visually impaired friend, I was struck by the irony that while I have been teaching robots to see, there are many people who cannot see and need help. This motivated me to build the visual assistance system with OpenCV's Artificial Intelligence Kit with Depth (OAK-D), powered by Intel." Why It Matters: The World Health Organisation estimates that globally 285 million people are visually impaired.

It's the year 2030, we are living in an age of increasing automation and artificially intelligent bots are powering this. All the trivial decisions are driven by machines and they are redesigning our ways of life. Lorem Ipsum is frustrated with the work lately and has a splitting headache, the automated recommendations from his Google health don't help and he gets an appointment with his doctor. It takes him just 30 minutes to go get through all the arduous stages of his brain test until he sees the doctor finally. "The scan results look negative for any abnormality, the brain health score is on the positive side", the doctor concludes it is just a headache and prescribes the medicines.