One of the most important business models of the 21st century is, without question, the platform model, which powers many of today’s biggest and most disruptive companies. Innovation platform, such as Google Android, Apple iPhone operating systems, and Amazon Web Services enable third-party firms to add complementary products and services to a core product or technology. Transaction platforms, which include the likes of Amazon Marketplace, Airbnb, and Uber, bring together producers and users in efficient exchanges of value, and they leverage network effects—the more participants, the greater the value produced. Read More
Daily Archives: December 18, 2019
7 Artificial Intelligence Trends to Watch in 2020
Artificial Intelligence offers great potential and, for some, risks for humans in the future. While still in its infancy it is being employed in some interesting ways. X
Here we explore some of the main AI trends predicted by experts in the field. If correct, 2020 should see some very exciting developments indeed. Read More
Self-training with Noisy Student improves ImageNet classification
We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 16.6% to 74.2%, reduces ImageNet-C mean corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 27.8 to 16.1.
To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as good as possible. But during the learning of the student, we inject noise such as data augmentation, dropout, stochastic depth to the student so that the noised student is forced to learn harder from the pseudo labels. Read More
There’s a new way to tame language AI so it doesn’t embarrass you
In the last two years, the AI subfield of natural-language processing has seen enormous progress. For example, a language model developed by the San Francisco–based research lab OpenAI, called GPT-2, has been used to generate fiction, fake news articles, and a practically infinite Choose Your Own Adventure–style text game.
But these kinds of models are essentially massive text-prediction systems that don’t take sense into account, so the sentences they produce are more likely to be superficially fluent than they are to be truly meaningful. It’s hard to tell a model to stick to a particular topic like health care, for example. Yet models like GPT-2 can still be gamed to produce racist and toxic output, making them even less useful. Read More
When Data Creates Competitive Advantage
Many executives and investors assume that it’s possible to use customer-data capabilities to gain an unbeatable competitive edge. The more customers you have, the more data you can gather, and that data, when analyzed with machine-learning tools, allows you to offer a better product that attracts more customers. You can then collect even more data and eventually marginalize your competitors in the same way that businesses with sizable network effects do. Or so the thinking goes. More often than not, this assumption is wrong. In most instances people grossly overestimate the advantage that data confers. en Data Creates Competitive Advantage.
The virtuous cycles generated by data-enabled learning may look similar to those of regular network effects, wherein an offering—like a social media platform—becomes more valuable as more people use it and ultimately garners a critical mass of users that shuts out competitors. But in practice regular network effects last longer and tend to be more powerful. To establish the strongest competitive position, you need them and data-enabled learning. However, few companies are able to develop both. Nevertheless under the right conditions customer-generated data can help you build competitive defenses, even if network effects aren’t present. In this article we’ll walk you through what those conditions are and explain how to evaluate whether they apply to your business. Read More
How do you describe what AI can really do?
Global AI Survey: AI proves its worth, but few scale impact
Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers shows the way.
Adoption of artificial intelligence (AI) continues to increase, and the technology is generating returns. 1 The findings of the latest McKinsey Global Survey on the subject show a nearly 25 percent year-over-year increase in the use of AI 2 in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business. 3 A majority of executives whose companies have adopted AI report that it has provided an uptick in revenue in the business areas where it is used, and 44 percent say AI has reduced costs. Read More
8 biggest AI trends of 2020, according to experts
Artificial intelligence is one of the fastest moving and least predictable industries. Just think about all the things that were inconceivable a few years back: deepfakes, AI-powered machine translation, bots that can master the most complicated games, etc.
But it never hurts to try our chances at predicting the future of AI. We asked scientists and AI thought leaders about what they think will happen in the AI space in the year to come. Here’s what you need to know. Read More
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