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.

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.

How businesses can use artificial intelligence (AI) to their advantage, perhaps even in a ... [ ] transformative way, without turning the pursuit of AI advantage into a quixotic quest Frankly, I was hoping an artificial intelligence (AI) algorithm would write this column for me, because who knows more about AI than the mysterious little gremlins that make "machine learning" possible? That, alas, didn't happen; so I'm on my own. Like most people in business, I don't need any convincing that artificial intelligence (for most companies in many areas of their operations) will become a game-changer. Still, it remains a fluid, if not amorphous, concept in many respects. What, exactly, can we expect AI to do for us that we're not already doing--or how will it improve what we're doing by doing it better, faster, cheaper, with greater insight or fewer errors?

You've seen the ways AI has been leveraged in recent years: Tesla has self-driving cars, chatbots answer customer service queries, and Siri can send a text for you or set a reminder. In 2018 we're likely to see AI permeate our daily lives in much more real ways. Everything from the way we make purchases to the way we create content is likely to see a significant transformation. Here are five artificial intelligence advancements that will work their way further into your life in 2018. Sure, you can already unlock the latest iPhones and confirm some payments with your face.

The days of just a piece of paper and a promise are long gone for the insurance industry. Policy holders, still skeptical of traditional insurance policies and hidden terms and conditions, increasingly expect transparency and efficiency from their insurance providers. That is exactly what they deserve. The technology behind Insurtech is making these changes possible and enabling mass transformation. Changing markets and customer demands have led the insurance industry to move away from an out-dated, policy-led framework to an industry that places the customer first. InsurTech has been at the heart of this transformation, noticeably in home insurance.

Inc. Echo Show 8 Alexa-enabled smart display sits on a table during an unveiling event ... [ ] at the company's headquarters in Seattle, Washington, U.S., on Wednesday, Sept. 25, 2019. Inc. defended the privacy features of its Alexa digital assistant -- and introduced some new tools to reassure users -- following months of debate about the practices of the technology giant and its largest competitors. Alexa might sound a little more natural when it responds to you from now on. Amazon is allowing developers to have the voice assistant respond to US users in "a happy/excited or a disappointed/empathetic tone" at various levels of intensity. The company suggested the tones might be especially effective for gaming and sports Alexa skills.

Can you imagine living in a world with no internet connection? Can you let go of your smartphone for even a few hours? And can you run a successful business without making use of the latest technologies? The answer is a resonant "No" in most cases. This is due to the great advancements made in technology over the years.

Amazon is helping to ramp-up the Christmas excitement this year by playing a daily message from Father Christmas to little ones who talk to Amazon Alexa. People who ask the device'how many days until Christmas?' will be told the answer and then asked by Alexa: 'Would you like to hear an update from my good friend Santa?' A message from the big man himself then plays with a jolly tone that is sure to build festive spirit over the next four weeks. In today's message Santa shared that he had been making mince pies with Mr Claus in the North Pole In the message he promises future updates and explains how he is preparing for his busiest day of the year. He was heard yesterday saying: 'Ho ho ho, I'm so looking forward to Christmas. I will be starting my daily Christmas update very soon so make sure you come back if you love Christmas, come back tomorrow for another Christmas update.'