Kaushik is a technical leader at Meta, and has over 10 years of experience building AI-driven products at companies like LinkedIn and Google. Shalvi is an AI scientist at SAP, and has experience as a data scientist, a software engineer, and project manager. Frank is a founding engineer at co:rise and started his career at Coursera, where he was the first engineering hire and built much of the platform's original core infrastructure. The following excerpts from Jake's conversation with Kaushik, Shalvi, and Frank have been edited and condensed for clarity. You can watch the complete recording here. Kaushik, you've been a hiring manager at some big companies. You get a lot of resumes. What are you looking for? What advice do you have for someone who's working on their resume and thinking about how to position themselves? Kaushik: In terms of skills, I'm looking for a practical knowledge of applying ML to build products. That's something I think you can't get from books -- you have to have some hands-on experience. I'm not necessarily looking for someone to have experience with specific tools or techniques, because those things are constantly changing. It's more that I want to know about the approach they took. Why did they use the tools they did, and what did they do when things got tricky or didn't work the first time? Don't get me wrong, I think having a good theoretical foundation is definitely necessary. But I would say you should spend as much time as you can solving real problems. That's how you learn which techniques work best for which use cases, and it will help you get a better understanding of the theoretical side, too. Kaushik: In terms of preparing for interviews, other than brushing up on the fundamentals, my advice would be to brainstorm a couple of problems that are relevant to the company you're interviewing with and do some background research on the common techniques to solve those problems.
Google Lens has been around for some time now as the search giant's de facto AI search for images and image-based text. Now, following rumors that suggested Lens for desktop platforms might be coming, searching Google via an image upload uses the Assistant-related feature too. That's based on recent reports following a roll-out on the company's search page. For clarity, that's searches found at images.google.com. The site is effectively Google's solution for reverse searching images.
Originally published in Snapchat Engineering, July 11, 2022. Snapchat ad ranking aims to serve the right ad to the right user at the right time. These are selected from millions of ads in our inventory at any time. We do so with a strong emphasis on maintaining an excellent user experience and upholding Snap's strong privacy principles and security standards, including honoring user privacy choices. Serving the right ad, in turn, generates value for our community of advertisers and Snapchatters.
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Twitch is the world's biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It's where millions of people come together to chat, interact, and make their own entertainment. You'll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We're on a quest to empower live communities, so if this sounds good to you, see what we're up to on LinkedIn and Twitter, get interviewing tips on Instagram, and discover projects we're solving on our Blog. Amazon Interactive Video Service (IVS) is a managed live video service, within Amazon Web Services (AWS), built on top of Twitch Video.
Children who grow up in low-income households but who make friends that come from higher-income homes are more likely to have higher salaries in adulthood than those who have fewer such friends. "There's been a lot of speculation… that the individuals' access to social capital, their social networks and the community they live in might matter a lot for a child's chance to rise out of poverty," says Raj Chetty at Harvard University. To find out how if that holds up, he and his colleagues analysed anonymised Facebook data belonging to 72.2 million people in the US between the ages of 25 and 44, accounting for 84 per cent of the age group's US population. It is relatively nationally representative of that age group, he says. The team used a machine learning algorithm to determine each person's socioeconomic status (SES), combining data such as the median income of people who live in the same region, the person's age, sex and the value of their phone model as a proxy for individual income.
When the richest man in the world is being sued by one of the most popular social media companies, it's news. But while most of the conversation about Elon Musk's attempt to cancel his $44 billion contract to buy Twitter is focusing on the legal, social, and business components, we need to keep an eye on how the discussion relates to one of tech industry's most buzzy products: artificial intelligence. The lawsuit shines a light on one of the most essential issues for the industry to tackle: What can and can't AI do, and what should and shouldn't AI do? The Twitter v Musk contretemps reveals a lot about the thinking about AI in tech and startup land – and raises issues about how we understand the deployment of the technology in areas ranging from credit checks to policing. At the core of Musk's claim for why he should be allowed out of his contract with Twitter is an allegation that the platform has done a poor job of identifying and removing spam accounts.
The past few years have seen a massive boom in smartphone technology and applications, mainly due to the rapid evolution in artificial intelligence and related technologies. AI has managed to completely transform the user interface and experience for even the simplest mobile apps that we use on a daily basis. In today's world, half of our daily tasks are automated and performed by mobile apps that are powered by AI, and it is truly a magical time to be alive. Speaking of artificial intelligence, it is impertinent to mention that the ever-growing phenomenon continues to morph mobile apps into something entirely different. From physical learning to AI-based learning, from physical piano lessons to online lessons for piano – everything is dominated by AI.
When the richest man in the world is being sued by one of the most popular social media companies, it's news. But while most of the conversation about Elon Musk's attempt to cancel his $44 billion contract to buy Twitter is focusing on the legal, social, and business components, we need to keep an eye on how the discussion relates to one of tech industry's most buzzy products: artificial intelligence. The lawsuit shines a light on one of the most essential issues for the industry to tackle: What can and can't AI do, and what should and shouldn't AI do? The Twitter v Musk contretemps reveals a lot about the thinking about AI in tech and startup land--and raises issues about how we understand the deployment of the technology in areas ranging from credit checks to policing. At the core of Musk's claim for why he should be allowed out of his contract with Twitter is an allegation that the platform has done a poor job of identifying and removing spam accounts.
For the past few months, I've seen quite a few people passing assessments and earning badges on LinkedIn. One in particular caught my eye. It was an assessment for machine learning. At the time, I was busy (and a little scared that I would fail), so I bookmarked it and went along my way. Recently, it popped up and I decided that I should take it to see what it is about.