Artificial Intelligence a Veracity Check
Artificial Intelligence (AI) is the new black, the new shiny thing. The answer to the prayer of all marketers, and the end of creativity. The recent emergence of artificial intelligence from the obscure hallways of academia. The artificial intelligence data science backrooms have been driven by stories of drones, robots, and driverless cars by tech giants like Amazon. Google and Tesla. But exaggeration goes beyond everyday reality.info technology hub
Artificial Intelligence history of development
Artificial intelligence has a fifty-year artificial intelligence history of development, experimentation, and reasoning in mathematics and computer science. It’s not an overnight sensation. What makes it exciting is the confluence of large data sets, improve platforms and software, faster and stronger processing capabilities. A growing pool of data scientists is eager to exploit a wide range of applications. The normal, everyday uses of AI and machine learning will make a greater difference in the lives of consumers. But brands than the glamorous applications promote in the press.
So consider this Artificial Intelligence reality test:
Big data is messy. We are artificial intelligence creating data and connecting large data sets at extraordinary speeds, doubling every year. The growth of mobile media, social media, apps, robotic personal assistants, wearables, electronic medical records, automobiles and self-reporting devices, and the upcoming Internet of Things (IoT) is creating enormous opportunities and challenges. In most cases, there is significant and time-consuming work of aligning, normalizing, completing, and correlating disparate data long before any analysis begins.
Artificial Intelligence Compiling
Collecting, storing, filtering, and associating these bits and bytes to any individual is complex and intrusive. Compiling the so-call “golden record” requires significant artificial intelligence computing power. A robust platform, fuzzy logic, or deep learning to connect disparate pieces of data and adequate privacy protection. It also requires significant modeling skills and a group of data scientists who are able to see the forest instead of the trees.
Artificial Intelligence personal communication
One by one is still ambitious. The dream of one-to-one artificial intelligence personal communication looms large. But it’s still ambitious. So Triggers are the need to develop common protocols to resolve identity and protect privacy. Understand individual permissions and sensitivities. Identify tipping points, and outline how consumers and individual sectors move across time and space in their journey of need. for brand preference.
Artificial Intelligence personalized recommendations
Predictive analytics for people awards. Amazon has train us to expect personalized artificial intelligence recommendations. We were brought up with the concept of “If you like this, you probably like it.” As a result, we expect favorite brands to know about us and use the data we share responsibly, knowingly and unknowingly, to make our lives easier, more convenient, and better. For consumers, predictive analytics works if the content is personally relevant, useful, and view as valuable. Anything less than that is SPAM.
Artificial Intelligence practical predictions
But making realistic and artificial intelligence practical predictions base on data is still more art than science. Humans are creatures of habits with some predictable patterns of attention and behavior. But we are not necessarily rational, often artificial intelligence inconsistent. Because quick to change our mind or change our course of action, and generally private. AI, which uses deep learning techniques in which the algorithm trains itself. Can help make sense of this data by monitoring actions over time, aligning behaviors with observable criteria, and assessing artificial intelligence anomalies.