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) …
AEye Inc has introduced iDAR, a new form of intelligent data collection that enables rapid, dynamic perception and path planning. AEye's iDAR is designed to intelligently prioritize and interrogate co-located pixels (2D) and voxels (3D) within a frame, enabling the system to target and identify objects within a scene 10-20x more effectively than LiDAR-only products. Additionally, iDAR is capable of overlaying 2D images on 3D point clouds for the creation of True Color LiDAR. Its embedded AI capabilities enable iDAR to utilize thousands of existing and custom computer vision algorithms, which add intelligence that can be leveraged by path planning software. The introduction of iDAR follows AEye's September demonstration of the first 360 degree, vehicle-mounted, solid-state LiDAR system with ranges up to 300 meters at high resolution.
This post is authored by Vani Mandava, Director of Data Science at Microsoft Research. The AI revolution is poised to unleash unprecedented innovation and impact on our society. Several research and development groups across Microsoft have hit their stride in delivering world-changing impact through the power of AI. Working together, we are creating a comprehensive Microsoft AI platform and a set of AI services that will enable the next generation of intelligent applications that will augment human intelligence. The AI buzz has been impossible to miss at numerous conferences that Microsoft has participated in during the past year.
Artificial intelligence is rapidly changing many aspects of how we work and live. Perhaps your holiday shopping involved some AI algorithms, as well.) But despite the constant flow of news, many misconceptions about AI remain, says Anthony Scriffignano, Ph.D., senior vice president and chief data scientist at Dun & Bradstreet. To properly harness the power of AI, he says, we need to let go of the wrong assumptions we've made about it. Here are three he believes are the biggest.
Improving cold calling efforts: Phone and emails are still a strong part of sales and marketing. But here, when it comes to cold calls, tracking, analyzing and improving them should be the main concern. The problem is, its difficult to do that. Within the realm of machine-learning now falls conversation intelligence. Companies like Marketo have started offering'Conversation Intelligence' solutions.
When living and operating in a market largely dominated by a vendor that isn't you, the strategy you must deploy is one of focus. In the early days of Power, IBM tried to take on Intel head to head and that just wasn't working. You can understand why IBM thought it could do this; it was once the most powerful company in the world. But, like Microsoft, Intel's strength largely came from providing technology to firms like IBM, and IBM's decline in the late 1980s and early 1990s not only weakened it substantially, it collectively strengthened other firms. Much like AMD, which has always been weaker than Intel, IBM needed to pick its battles, and given that the company still pretty much owns the market for enterprise-class AI with Watson, and that this segment is slated to become the most lucrative in the industry for servers over the next decade, it chose wisely to make this one of its critical areas of focus.
Byron Reese: This is "Voices in AI", brought to you by Gigaom. Today our guest is Gregory Piatetsky. He's a leading voice in Business Analytics, Data Mining, and Data Science. Twenty years ago, he founded and continues to operate a site called KDnuggets about knowledge discovery. It's dedicated to the various topics he's interested in.
Human intelligence is hard enough to measure, and over the decades today's ubiquitous Intelligence Quotient, or "IQ," test, the standard by which we are all judged, has caused ferocious controversy. But today we have a new, potentially even thornier dilemma – how we assess the intelligence of today's and tomorrow's increasingly capable Artificial Intelligence (AI) agents, and perhaps, one day, even the Avatars and robots that they'll inhabit. While there are those who argue we shouldn't even bother trying to measure AI's IQ, whether it's because AI is seen as an "rapidly evolving alien, artificial and synthetic" form of intelligence by nature that is "dramatically different to human intelligence," or because today there are already so many different variants and variations of AI it makes "one standard to rule them all" almost impossible to define, being able to measure things seems deeply engrained into human behaviour. Therefore, it's obviously inevitable that at some point we will find ourselves adopting a new standard, an IQ test for AI that "once and for all" can tell us if we are in fact dumber than the 10,000 IQ chip in our trainers which is slated by Softbank CEO Masayoshi Son, who now owns ARM, to arrive by 2047. Over the decades there have been a number of attempts by companies, such as Facebook who recently wrote a white paper on how to "Evaluate the intelligence of AI," and individuals, such as Alan Turning with his Turing Test, to create an standards based test but in the main very few of them have been hailed as credible.
Our research partners at The Institute for the Future (IFTF) recently forecasted that we're entering the next era of human machine partnership, and that between now and 2030 humans and machines will work in closer concert with each other, transforming our lives. We've worked with machines for centuries, but we're about to enter an entirely new phase – characterized by even greater efficiency, unity and possibility than ever before. Emerging technologies, such as Artificial Intelligence (AI), Augmented Reality (AR), Virtual Reality (VR), and advances in Internet of Things (IoT) and cloud computing – made possible through exponential developments in software, analytics, and processing power – are augmenting and accelerating this direction. This is evident in our connected cars, homes, business and banking transactions already; even transforming how farmers manage their crops and cattle. Given this dizzying pace of progress, let's take a look at what's coming down the pike next.
François Chollet argues about the Impossibility of an Intelligence Explosion. It is a strong article with the exception of the conclusion. Chollet is accurate in describing the many of the obstacles that we expect to encounter in creating an advanced artificial general intelligence (AGI). These obstacles are as follows ( I use my own categorization, but its mapping with Chollet's should be straightforward): The flaw in Chollet's article is that he believes the pace to be linear. There is little evidence that this is true.