Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.

Here's an interesting stat from the Pew Research Center: more than half of smart speaker owners in the US (54 percent) report saying "please" at least occasionally to their AI assistants, with one-in-five (19 percent) saying please frequently. Curiously, the question of AI politeness also breaks down along gender lines, with 62 percent of women reporting that they say "please" at least sometimes, versus 45 percent for men. One possible answer is that men are generally ruder to women, and this latter category now includes AI assistants coded as female. Experts have long noted that the design choices for AI bots could have misogynist effects by reinforcing gender stereotypes. "Because the speech of most voice assistants is female, it sends a signal that women are ... docile and eager-to-please helper," a report from the UN noted earlier this year.

The growth of ecommerce in the recent past can only be described as explosive and sweeping across the planet. According to a 2016 study, half of all dollars spent online in America belong to Amazon. And consider this, Recommendation Engines alone drive 35% of that revenue. But it is not ecommerce alone that's reaping the huge benefits that recommendation engines have to offer. Direct to device streaming services such as Netflix, Spotify among others, analyze user behavior almost to a micro moment level, then gather data surrounding similar users who are likely to buy the same items based on their browsing history, and provide that much needed nudge to move on to the next purchase on the platform.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology. I've been reading Artificial Intelligence in Education, a 2019 publication by Wayne Holmes, Maya Bialik, and Charles Fadel, that explores implications of AI in the realm of teaching and learning.

And they're coming for your business -- with the power to build or destroy your ability to compete in the near future. "Those companies not considering investing and innovating will soon be outperformed by the new economy that runs on machine learning" Machine learning is already changing the world. As a key subset of artificial intelligence (AI), it enables computers to act and learn on their own, without being specifically programmed, by utilizing data and experience rather than being explicitly programmed. Self-driving cars, Netflix recommendations, and virtual personal assistants like Siri and Alexa are some of AI's best-known applications. One of the most immediate ways businesses use machine learning to improve their competitiveness is by automating back-office processes, the majority of which are high volume, rules-based functions that could seamlessly operate on a "lights out" basis, freeing up employees' time for achieving more strategic company objectives.

In early 2018, Abbott became the first in Indian pharmaceutical industry to deploy an AI assistant for its field force.

Today Artificial Intelligence and Machine Learning are penetrating every aspect of business, from Chatbots being deployed to assist customers to AI-driven platforms being harnessed to automate sales processes. From powering Apple's Siri and Microsoft's Cortana to Google's Allo, AI is promising a better future. At Codea, we offers AI consulting services in Newyork,USA and help businesses build cutting-edge AI solutions that enable them to achieve a first-mover advantage to be a leader in the better future.

Meet Ivey! Ivey is an Intelligent Virtual Entity (IVE) who is designed to work as a marketing assistant at a major corporation. As an AI-powered virtual employee, she carries out certain pre-scheduled tasks on a regular basis--but she does much more than just that. Ivey can understand the virtual environment she works in, prioritize and schedule her own tasks, and interact with her "boss" John using natural language. She can perform complex analytical processes, and -- much like a human employee -- she can learn and improve based on feedback from her environment (and her boss). What does this look like in practical terms?

Online learning to rank (LTR) focuses on learning a policy from user interactions that builds a list of items sorted in decreasing order of the item utility. It is a core area in modern interactive systems, such as search engines, recommender systems, or conversational assistants. Previous online LTR approaches either assume the relevance of an item in the list to be independent of other items in the list or the relevance of an item to be a submodular function of the utility of the list. The former type of approach may result in a list of low diversity that has relevant items covering the same aspects, while the latter approaches may lead to a highly diversified list but with some non-relevant items. In this paper, we study an online LTR problem that considers both item relevance and topical diversity. We assume cascading user behavior, where a user browses the displayed list of items from top to bottom and clicks the first attractive item and stops browsing the rest. We propose a hybrid contextual bandit approach, called CascadeHybrid, for solving this problem. CascadeHybrid models item relevance and topical diversity using two independent functions and simultaneously learns those functions from user click feedback. We derive a gap-free bound on the n-step regret of CascadeHybrid. We conduct experiments to evaluate CascadeHybrid on the MovieLens and Yahoo music datasets. Our experimental results show that CascadeHybrid outperforms the baselines on both datasets.