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 →Machine learning is going real-time: Here’s why and how
Organizations are turning to machine learning because of the return on investment. The ones doing it in real-time are topping the charts.
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”.
Read More →LinkedIn and Intel tech leaders on the state of AI
AI is on a roll. Adoption is increasing across the board, and organizations are already seeing tangible benefits. However, the definition of what AI is and what it can do is up for grabs, and the investment required to make it work isn’t always easy to justify. Despite AI’s newfound practicality, there’s still a long way to go.
Let’s take a tour through the past, present, and future of AI, and learn from leaders and innovators from LinkedIn, Intel Labs, and cutting-edge research institutes.
Read More →Cloud, microservices, and data mess? Graph, ontology, and application fabric to the rescue.
Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb.
Read More →DeepMind is developing one algorithm to rule them all
DeepMind wants to enable neural networks to emulate algorithms to get the best of both worlds, and it’s using Google Maps as a testbed.
Read More →Machine learning at the edge: A hardware and software ecosystem
Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market. For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.
Read More →DeepMind aims to marry deep learning and classic algorithms
Will deep learning really live up to its promise? We don’t actually know. But if it’s going to, it will have to assimilate how classical computer science algorithms work. This is what DeepMind is working on, and its success is important to the eventual uptake of neural networks in wider commercial applications.
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