What the Public Relations Industry Gets Wrong About Artificial Intelligence


Artificial intelligence has promised to revolutionize our lives, taking over the mundane tasks of daily existence, from prewriting "smart" email replies to driving our car through rush hour traffic. In the PR realm, AI has been touted as equal parts something to celebrate (no more manual coverage reports!) and fear (er, so long, means of employment). But the truth, as usual, lies somewhere in between. Some form of intelligent technology is already embedded in the PR industry, from the tools we use to find new audiences and monitor evolving conversations to modern media placement. Bloomberg News uses AI to generate coverage on some 3,500 earnings reports every quarter.

Artificial intelligence predicts which movies will succeed--and fail--simply from plot summaries


Artificial intelligence (AI) still can't see the future, but a new algorithm may come close: Using nothing but written movie summaries, the AI can consistently tell which films will play well--or rottenly--to critics and audiences. If the model can be further refined, it could one day help producers predict whether a movie will be a flop at the box office, before it's even made. To test several models, researchers used plot summaries of 42,306 movies from all over the world, many collected from Wikipedia. The models broke up the summaries by sentence and used something called sentiment analysis to analyze each one. Sentences considered "positive," such as "Thor loves his hammer," would receive a rating closer to one.

How to Improve Your Website with Artificial Intelligence ZoomInfo Blog


Artificial Intelligence--or AI--has become an increasingly hot topic in the marketing world as of late. The reason for this is simple: AI technology can automate tasks, simplify complex processes, and organize complicated data sets just as a real marketing professional would--only faster and more accurately. Early on in the days of artificial intelligence, this idea of automation seemed threatening to the human workforce. But, we're of the mindset that AI simply enables humans to do better, more informed work. As it turns out, most businesses agree.

Delivering 360 customer service through AI, automation and the all-important human touch -


"The future is digital", is a mantra no one would argue with. In recent years, we've really started to see how the practical applications of AI and automation can empower us as consumers. It streamlines many aspects of our interactions with the organisations that provide our goods and services. Through apps, chatbots, and automated services, we can now make secure transactions, change settings and get most of the information we need at the touch of a button. All without having to talk in person with supplier organisations.

Pronounced Applications of Artificial Intelligence in Retail Banking Analytics Insight


A variety of business processes under the umbrella of retail banking are the constructive consequence of AI and automation services. Not only the payment processing automation and fraud detection but banks are also getting benefitted by automated credit scoring and customer service chatbots. Fraud detection, credit scoring, and chatbots turn out to be the major beneficiation of the retail banking system. The banks install anomaly detection software to their system which is trained in real-time on a range of labeled data retrieved from transactions and loan applications. The ML algorithms analyze every single bit of data before it can be labeled under fraud case.

Study: Seniors talk with AI chatbots more when the conversation is deeper


Luminaries like former Google CEO Eric Schmidt and iRobot CTO Helen Greiner expect that one day, loquacious AI will provide companionship for the roughly 40% of elderly people who say they regularly experience loneliness. If this vision comes to pass, it'd be no less than transformative from a wellness perspective -- loneliness has been found to increase the likelihood of mortality by 26%, and lonely people have a 64% increased chance of developing clinical dementia. That's perhaps why researchers at the University of Rochester investigated interactions between older adults and an AI-imbued digital avatar. As they explain in a paper published on the preprint server They say that this suggests avatars could provide "valuable practice" and coaching to help older adults navigate a challenging conversation and improve both their health and quality of life.

Discourse Behavior of Older Adults Interacting With a Dialogue Agent Competent in Multiple Topics Artificial Intelligence

We present some results concerning the dialogue behavior and inferred sentiment of a group of older adults interacting with a computer-based avatar. Our avatar is unique in its ability to hold natural dialogues on a wide range of everyday topics---27 topics in three groups, developed with the help of gerontologists. The three groups vary in ``degrees of intimacy", and as such in degrees of difficulty for the user. Each participant interacted with the avatar for 7-9 sessions over a period of 3-4 weeks; analysis of the dialogues reveals correlations such as greater verbosity for more difficult topics, increasing verbosity with successive sessions, especially for more difficult topics, stronger sentiment on topics concerned with life goals rather than routine activities, and stronger self-disclosure for more intimate topics. In addition to their intrinsic interest, these results also reflect positively on the sophistication of our dialogue system.

Five Things AI May Know Before You Do


But AI shouldn't conjure images of robots taking over. Instead, the future of work will include machines handling tasks employees don't want to or don't have the resources to do. AI can expand a person's capacity to do the work that really matters and can help companies better understand workers. Here are five things AI tools may notice before employees do. As much as everyone hates to think they're being spied on at work, it's already happening by way of Natural Language Processing (NLP).

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog Artificial Intelligence

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critical shortcomings for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms, which are able to effectively learn offline, without exploring, from a fixed batch of human interaction data. We leverage models pre-trained on data as a strong prior, and use KL-control to penalize divergence from this prior during RL training. We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. The algorithms are tested on the problem of open-domain dialog generation -- a challenging reinforcement learning problem with a 20,000-dimensional action space. Using our Way Off-Policy algorithm, we can extract multiple different reward functions post-hoc from collected human interaction data, and learn effectively from all of these. We test the real-world generalization of these systems by deploying them live to converse with humans in an open-domain setting, and demonstrate that our algorithm achieves significant improvements over prior methods in off-policy batch RL.

A Guide to Using AI for Marketing


As technology increases, so do the tools that we as marketers have at our disposal. One of the more recent developments, artificial intelligence (AI), is no exception. While most just hear AI and think of self-driving cars or sentient robots, this is an area of science that we can use to help us be more effective as marketers. AI is the method of using machine learning to mimic human intelligence, patterns and tendencies. Computers use algorithms and historical data to determine how to respond to certain actions.