Most people associate artificial intelligence and machine learning with futuristic applications like Terminator, Hal or Samantha, but applications using AI and ML are more common than you think. We have Siri, Alexa and Google Assistant. The ML algorithm recommends movies on Netflix, and we shouldn't forget about the Tesla self-driving car.
All of a sudden, AI is everywhere--in consumer products, in our entertainment, in our consciousness. Every day we hear stories from Google, Uber, Baidu, Microsoft, Amazon and others about unprecedented achievements in language translation, gaming, image recognition, music composition, beer-delivering driverless trucks, and a host of achievements, all powered by AI. But what is AI, really? And, to be fair, do we even understand what intelligence is? In 1983, and long before what we think of now as the Internet, a developmental psychologist named Howard Gardner published what is now a seminal work on the "Theory of Multiple Intelligences".
With text analytics, various burning questions around the'why' and'what' of a piece or group of content can be answered. Examples like social media chatter around brand can create a supremely spiraling impact (remember the post which showed a Kentucky man was violently removed from his United Airlines seat on an overbooked flight? And how it lead to a social media disaster for the airline?). Hence there need to be ways to make sense of the unstructured data from diverse sources.
Artificial intelligence has shaken off its experimental mantra and fully launched into the developmental phase of the innovation cycle. As the application of AI grows increasingly widespread across almost all verticals, several industries are now dependent on the advancements of AI to ensure their growth and competitiveness. For instance, autonomous vehicle technology, IoT, healthtech, e-commerce, insurance and smart cities are all hot industries hungry for artificial intelligence that can propel their next technology push, improve efficiency and productivity, streamline business models, and reduce latency and fragmentation.
Attend this webinar to learn how you can determine which threats pose the greatest danger to your AI, machine learning, natural language processing, and other advanced analytics technologies make up the leading-edge of how organizations are gaining value from big data. Here's a collection of some of the most influential and rising stars on Twitter when it comes to AI and machine learning. Artificial intelligence, machine learning, natural language processing, and other advanced analytics technologies are driving the growth of some of the most forward-looking and admired organizations, from Amazon to Netflix to GE. This leading-edge of analytics combines big data and near real-time processing with other advanced tools to deliver the insights you need, before an analyst might have even formulated the right question to ask. Some early use cases that have gotten a lot of attention include autonomous vehicles (self-driving cars), customer service chatbots, and recommendation engines.