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.
Read More →The state of MLOps in 2021
MLOps is the art and science of bringing machine learning to production, and it means many things to many people. The State of MLOps is an effort to define and monitor this market.
Read More →AI ethics in the real world: FTC commissioner shows a path toward economic justice
FTC Commissioner Rebecca Kelly Slaughter lays out machine learning and AI's potential for harm and outlines some ways for the FTC to counter it.
Read More →Building MLGUI, user interfaces for machine learning applications
Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI (Machine Learning Graphical User Interface)?
Read More →Open-source growth and venture capital investment: Data, databases, challenges, and opportunities
Open-source software used to be poorly understood by commercial forces, and it's often approached in a biased way. A new generation of investment funds goes to show that things are changing.
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