If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As AI becomes more advanced, more and more aspects of our daily life are touched by invisible algorithms. However, the more we entrust vital decisions to software, the greater the need becomes to interrogate how they work, and why they reach the conclusions they do. Concern has been slowly bubbling, with the book'Weapons of Math Destruction' by Cathy O'Neil highlighting the ways in which these algorithms can influence crucial decision making processes including whether to grant a loan, who to hire, college admissions, and bail decisions. One of the most potent dangers of algorithms is how they incorporate and perpetuate intentional and unintentional bias. Rachel Bellamy leads the IBM Research Human-Agent Collaboration group which examines, among other things, cognitive bias and how it's coded into AI.
Datasets are the lifeblood of artificial intelligence (AI) -- they're what make models tick, so to speak. But data without corresponding annotations is, depending on the type of algorithm at play (i.e., supervised versus unsupervised), more or less useless. That's why sample-labeling startups like Scale have raised tens of millions of dollars and attracted clients like Uber and General Motors. And it's why Kevin Guo and Dmitriy Karpman cofounded Hive, a startup that uses annotated data supplied by hundreds of thousands of volunteers to train domain-specific AI models. Hive, which employs nearly 100 people, launched its flagship trio of products -- Hive Data, Hive Predict, and Hive Enterprise -- shortly before raising over $30 million in venture capital from PayPal founder Peter Thiel's Founders Fund and others.
The idea of an artificial intelligence (AI) arms race between China and the United States is ubiquitous. Before 2016, there were fewer than 300 Google results for "AI arms race" and only a handful of articles that mentioned the phrase. Today, an article on the subject gets added to LexisNexis virtually every week, and Googling the term yields more than 50,000 hits. Some even warn of an AI Cold War. One question that looms large in these discussions is if China has, or will soon have, an edge over the United States in AI technology.
There are several companies claiming to offer AI solutions to consumer packaged goods (CPG) companies. AI solutions for business problems in the CPG industry appear to be less legitimate than we first thought. All of the companies discussed in this report employ relatively credentialed people in their C-suites, but their AI experience is generally lacking compared to other sectors we've covered (in terms of AI-related talent density, and experience actually using AI). The companies we examine in this report are older firms, who, unlike some of their startup competition, have no founding team members or C-level leadership with a strong background in AI. Many of the firms featured in this article, however, have hired experts in AI to run their AI practices and build AI-related products and services, but others have not hired any such experts to back up their claims of AI use.
The majority of health care executives (91 percent) are confident they will see a return on investment (ROI) on artificial intelligence investments, although not immediately, and foresee the greatest impact of AI will be on improving health care, according to an OptumIQ survey. Most (94 percent) health care leaders responded that their organizations continue to invest in and make progress in implementing AI, with 75 percent of healthcare organizations say they are implementing AI or have plans to execute an AI strategy, based on OptumIQ's survey of 500 senior U.S. healthcare industry executives, primarily from hospitals clinics and health systems, life sciences organizations, health plans and employers. OptumIQ is the intelligence arm of data and analytics of Optum, an information and technology-enabled health services business that is part of UnitedHealth Group. While many healthcare organizations have plans, progress is mixed across sectors. Of the 75 percent who are implementing AI or have plans to execute an AI strategy, 42 percent of those organizations have a strategy but have not yet implemented it.
Graph technologies are the scaffolding for building intelligent applications, enabling more accurate predictions and faster decisions. In fact, graphs are underpinning a wide variety of artificial intelligence (AI) use cases. This article series is designed to help you better leverage graph analytics to effectively innovate and develop intelligent applications faster. Last week, we looked at a variety of use cases for graph analytics, from real-time fraud detection to recommendation engines. This week, we'll delve deeper into a few of the ways that a graph database like Neo4j supports numerous AI use cases.
Facebook's had a rough year. So it's not exactly ideal timing for Facebook to launch the Portal and Portal, a line of smart home hubs with displays and cameras for making video calls to other Facebook users. Facebook's issues with cybersecurity and transparency are likely to kill both devices in their cradles, especially when considering the superior rival products already on the market. Both the Portal and larger Portal are interesting smart home gadgets, and boast a surprising level of refinement on the hardware end. The smaller Portal is similar to smart home devices from Amazon and Google.
Amazon boss Jeff Bezos has warned staff not to be complacent, claiming the firm'is not too big to fail' At an all-hands meeting last Thursday in Seattle, days before the firm announced the winners of its HQ2 contest, Bezos was asked about the recent failures of giant retailers like Sears. 'Amazon is not too big to fail,' Bezos said, in a recording of the meeting CNBC said it had heard. 'In fact, I predict one day Amazon will fail. If you look at large companies, their lifespans tend to be 30-plus years, not a hundred-plus years.' Bezos told the meeting the key to survival is to'obsess over customers'.
Microsoft has released a new app that aims to demonstrate how its Windows Machine Learning APIs can be used to build apps and "make machine learning fun and approachable." Emoji8 is a UWP app that uses machine learning to determine how well you can imitate emojis. As you make your best efforts to imitate a random selection of emojis in front of your webcam, Emoji8 will evaluate your attempts locally using the FER Emotion Recognition model, a neural network for recognizing emotion in faces. You'll then be able to tweet a gif of your top scoring images. "This app will give you a great end-to-end example of how you can use the Windows ML APIs to create simple yet magical experiences," the company said.