LinkedIn on the ‘Great Reshuffle’: Green skills, green jobs, and blind spots
LinkedIn embarked on an ambitious analysis of data from its nearly 800 million members worldwide to derive insights on the green transition. But while useful, the analysis comes with its blind spots, too.
Read More →Graph data science: What you need to know
Whether you’re genuinely interested in getting insights and solving problems using data, or just attracted by what has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor, chances are you’re familiar with data science. But what about graph data science?
Read More →Google sets the bar for AI language models with PaLM
Google’s new large language model (LLM) called PaLM (Pathways Language Model) is the first outcome of Pathways, Google’s new AI architecture, which aims to handle many tasks at once, learn new tasks quickly and reflect a better understanding of the world.
Read More →Massaging AI language models for fun, profit and ethics
Do AI language models really demonstrate intelligence? What about morality? Is it ok to tweak them, and if yes, who gets to do this, and how do the rest of us know?
Read More →Trustworthy AI: How to ensure trust and ethics in AI
A pragmatic and direct approach to ethics and trust in artificial intelligence (AI) — who would not want that? This is how Beena Ammanath describes her new book, Trustworthy AI.
Read More →Andrew Ng predicts the next 10 years in AI
Did you ever feel you’ve had enough of your current line of work and wanted to shift gears? If you have, you’re definitely not alone. Besides taking part in the Great Resignation, however, there are also less radical approaches, like the one Andrew Ng is taking.
Read More →How Netflix built its real-time data infrastructure
What makes Netflix, Netflix? Creating compelling original programming, analyzing its user data to serve subscribers better, and letting people consume content in the ways they prefer, according to Investopedia’s analysis.
Read More →The state of AI ethics: The principles, the tools, the regulations
What do we talk about when we talk about AI ethics? Just like AI itself, definitions for AI ethics seem to abound. A definition that seems to have garnered some consensus is that AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technologies.
Read More →Working from home means working different hours, but not necessarily more
The National Bureau of Economic Research analyzed the side effects of remote work using data from GitHub.
Read More →What will applied AI look like in 2022?
AI adoption has skyrocketed throughout the last 18 months. Besides Joe McKendrick, who wrote the foundational piece on HBR, professionals who work on AI would readily attest to this statement. Google search seems to be in on this not-so-secret too: When prompted with “AI adoption,” its auto-complete spurts out “skyrocketed over the last 18 months”.
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