During a conversation, there can come certain moments where its outcome hangs in the balance. In these pivotal moments, how one responds can put the conversation on substantially different trajectories leading to significantly different outcomes. Systems that can detect when such moments arise could assist conversationalists in domains with highly consequential outcomes, such as mental health crisis counseling. In this work, we introduce an unsupervised computational method for detecting such pivotal moments as they happen, in an online fashion. Our approach relies on the intuition that a moment is pivotal if our expectation of the outcome varies widely depending on what might be said next. By applying our method to crisis counseling conversations, we first validate it by showing that it aligns with human perception -- counselors take significantly longer to respond during moments detected by our method -- and with the eventual conversational trajectory -- which is more likely to change course at these times. We then use our framework to explore the relation of the counselor's response during pivotal moments with the eventual outcome of the session.

Medical students are losing the dexterity to stitch up their patients because they are spending too much time in front of screens. That's the claim from one professor of surgery who says trainee doctors are "less competent", compared to their older colleagues, at using their hands. Professor Roger Kneebone, from Imperial College London, says young people have so little experience of craft skills that they struggle with practical tasks. It's a worrying thought - but how true is it? Does screen time impact dexterity?

Deciding whom to help first can be a life-or-death decision. At the Crisis Text Line, a text messaging-based crisis counseling hotline, these deluges have the potential to overwhelm the human staff. So data scientists at Crisis Text Line are using machine learning, a type of artificial intelligence, to pull out the words and emojis that can signal a person at higher risk of suicide ideation or self-harm. The computer tells them who on hold needs to jump to the front of the line to be helped. They can do this because Crisis Text Line does something radical for a crisis counseling service: It collects a massive amount of data on the 30 million texts it has exchanged with users.

Deciding whom to help first can be a life-or-death decision. At the Crisis Text Line, a text messaging-based crisis counseling hotline, these deluges have the potential to overwhelm the human staff. So data scientists at Crisis Text Line are using machine learning, a type of artificial intelligence, to pull out the words and emojis that signal a person at higher risk of suicide ideation or self-harm. The computer tells them who on hold needs to jump to the front of the line to be rescued. They can do this because Crisis Text Line does something radical for a crisis counseling service: It collects a massive amount of data on the 30 million texts it has exchanged with users.

Chatbots and virtual assistants are getting a lot of attention in the AI world these days, and for good reason, as some of these tools are developing impressive skills. But there's more to the technology than conversational ability, and enterprises are finding value implementing component pieces of virtual assistants -- automated bots and natural language processing -- in other ways. "If it's hard for Siri to figure out [natural language chat], it's going to be hard for us," said Brian Canty, CEO and co-founder of Lea, a San Francisco-based company that has developed a Facebook messenger bot. Lea -- which stands for live event assistant -- delivers personalized concert recommendations to users. The original idea behind Lea was to build a conversational agent that users could chat with to explain what they're interested in, Canty said.

Natural language processing (NLP) allows us to tag, parse, and even extract information from text. But we believe it also has the potential to help address major challenges facing the world. Recently, we have been working on applying NLP to a serious global health issue: mental illness. Fortunately, mental health conditions can often be treated with counseling and psychotherapy, and in recent years there has been rapid growth in the availability of these treatments thanks to technology-mediated counseling. The goal of our project was to better understand how to conduct counseling sessions, which we have done through a large-scale study of crisis counseling conversations.

What is Crisis Text Line? Crisis Text Line provides free, 24/7 emotional support and information to people in crisis, by text. We aim to move every texter in crisis from a hot moment to a cool calm. Crisis Text Line is growing at an incredible rate. Since launching in August of 2013, our Crisis Counselors have exchanged over 46 million messages with texters in crisis.

Last month, in collaboration with the folks behind the fantastic Burner app, my company Voxable launched our latest chatbot project, Ghostbot for Burner. Ghostbot runs on top of Burner's "open" phone numbers as an agent inside your text message inbox, allowing you to "ghost" away from fleeting relationships by responding to unwanted or creepy texters on your behalf. Since the launch, Ghostbot has been covered in over one hundred media outlets. Chris Messina was also kind enough to post it to Product Hunt, calling it "the first bot-as-personal-firewall" he had seen. The opportunity to break new ground in the coming era of bot-mediated communication was what initially both excited and terrified us about the project.

Natural language processing (NLP) allows us to tag, parse, and even extract information from text. But we believe it also has the potential to help address major challenges facing the world. Recently, we have been working on applying NLP to a serious global health issue: mental illness. Fortunately, mental health conditions can often be treated with counseling and psychotherapy, and in recent years there has been rapid growth in the availability of these treatments thanks to technology-mediated counseling. The goal of our project was to better understand how to conduct counseling sessions, which we have done through a large-scale study of crisis counseling conversations.