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From Big Data to Hardware, Here’s What You Need to Know about AI

Google is Democratizing AI and is selling simple “computer vision software” to companies that are AI novices, here’s Why quantum physics will drive AI adoption, and how AI made CES relevant again.

Included here is your weekly digital and mobile advertising news round-up. See anything we might have missed. Drop us a line.

Google Sells A.I. for Building A.I. (Novices Welcome) [New York Times]

The new service is part of a widespread effort to expand the power of modern A.I. to businesses that are largely unfamiliar with this rapidly evolving technology. Like Google, a New York start-up called Clarifai offers an online service that helps customers train computer vision algorithms. At the same time, several other start-ups, like Boston’s DataRobot and Silicon Valley’s H2O.ai, offer services designed to help businesses analyze the way products, customers, markets and employees behaved in the past and predict how they will perform in the future.

DefinedCrowd’s next-gen platform solves the AI data acquisition problem [Tech Crunch]

Today, the company is publicly unveiling its next-generation SaaS platform for data scientists. Using the platform, users can use both a UI and an API to search for and select appropriate datasets for their applications. The company focuses on three horizontal areas: voice recognition, natural language processing, and computational imagery.

Machine learning models require DevOps-style workflows [Search Business Analytics]

Big data is driving the use of AI and machine learning. But teams must be swift to embrace DevOps and re-evaluate models, according to Wikibon’s James Kobielus.

Forget algorithms. The future of AI is hardware! [Huff Post]

But the true big bang in computing will come not from neuromorphic chips (which may end up having only niche applications despite the big promise), but from harnessing quantum physics.

How to predict and solve supply chain problems before they happen [Supply Chain Dive]

Supply chain analytics are all the rage, but can they truly predict the future? Yes, says Monte Zweben, CEO of Splice Machine — a San Francisco-based company that has created a platform for predictive applications that uses analytical processing and machine learning to improve over time. Online Predictive Processing (OLPP) can help manage operational processes and large-scale IoT infrastructures.

It’s time for Washington to start working on artificial intelligence [Tech Crunch]

By Congressman John K Delaney– As the founder of the Artificial Intelligence Caucus, I’ve been working to start a new dialogue on Capitol Hill that is focused on the future. Recently, I introduced the House version of the FUTURE of AI Act that would create a formal process for both Congress and the Executive Branch to start looking at AI seriously, asking hard questions and consulting experts on what the next steps should be.

How Amazon And Google’s AI Assistant War Made CES Relevant Again [Fast Company]

By connecting the sea of CES gadgets to much larger ecosystems, Amazon and Google have presented a vision in which voice commands are available pretty much anywhere, and can control practically any device.

How ML and AI will transform business intelligence and analytics [ZDNet]

Machine learning and artificial intelligence advances in five areas will ease data prep, discovery, analysis, prediction, and data-driven decision making.

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Machines Just Beat Humans on a Stanford Reading Comprehension Test [Futurism]

Why Artificial Intelligence Will Widen the Wealth Gap [HuffPost]

Automotive Retail With Artificial Intelligence: The Future Is Here [MediaPost]

CHINA WINNING ARTIFICIAL INTELLIGENCE WAR AGAINST U.S. [Newsweek]

61% of businesses have already implemented AI [TechRepublic]

Kuehne + Nagel looks to invest in logistics tech startups [Supply Chain Dive]

IBM May Finally Stop Shrinking. But Is It a Turnaround? [NY Times]

AI Supply Chain: Four Key Predictions for 2018 [Port Technology]

Artificial intelligence dominated the Consumer Electronics Show [The Economist]

Machine Learning in Finance: Challenges, Successes & Opportunities [Inside Big Data]