Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have developed a deep learning (DL) dialogue system called Serena. The system consists of a core generative model and post-processing algorithms. The core generative model is a 2.7 billion parameter Seq2Seq Transformer fine-tuned on thousands of transcripts of person-centered-therapy (PCT) sessions. The series of post-processing algorithms detects contradictions, improves coherency, and removes repetitive answers. Serena is implemented and deployed on \url{https://serena.chat}, which currently offers limited free services. While the dialogue system is capable of responding in a qualitatively empathetic and engaging manner, occasionally it displays hallucination and long-term incoherence. Overall, we demonstrate that a deep learning mental health dialogue system has the potential to provide a low-cost and effective complement to traditional human counselors with less barriers to access.

We introduce TVStoryGen, a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVStoryGen contains stories that are authored by professional screen-writers and that feature complex interactions among multiple characters. Generating stories in TVStoryGen requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, we propose to train reverse models on our dataset for evaluating the faithfulness of generated stories. We create TVStoryGen from fan-contributed websites, which allows us to collect 26k episode recaps with 1868.7 tokens on average. Empirically, we take a hierarchical story generation approach and find that the neural model that uses oracle content selectors for character descriptions demonstrates the best performance on automatic metrics, showing the potential of our dataset to inspire future research on story generation with constraints. Qualitative analysis shows that the best-performing model sometimes generates content that is unfaithful to the short summaries, suggesting promising directions for future work.

Lutron makes one of our favorite motorized shades, but the company also offers motorized blinds. Window blinds are considered "hard" window coverings because they consist of slats--wooden, in this case--that drop down from the top of the window (or that slide left or right, in the case of vertical blinds). The motor mounted in the headrail of the Serena blinds tilts the 2-inch slats for privacy and light control. The accumulated weight of the slats, however, makes them too heavy for the motor to lift--even though Lutron fabricates the slats from a soft, fine-grained timber called North American basswood. If you want to fully expose the window, you will need to lift the blinds by hand and pull them back down to close.

Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.

Has AI raised the floor but not the ceiling with marketing? Have we over-indexed on having content at scale? And is there a way for marketers to understand when hyper-personalization will cross the line into creepiness? In this episode of thinkPod, we are joined by Kate Bradley Chernis (Founder & CEO of Lately) and Joey Camire (principal & founding team of Sylvain Labs). We talk to Kate and Joey about whether AI will replace human marketers, where we are currently with AI and marketing, the difficulty of getting marketers to write, and how AI can bring delight to consumers. We also get into the hot debate around a company's responsibility with user data and imagine a future where each cup of yogurt is tracked. "AI as it relates to marketing is raising the floor. It doesn't totally feel like it's currently raising the ceiling." "I'm here to tell you that when marketing, it'll never replace humans altogether because it just doesn't work. "AI is not at the place right now where it's saying like, well, based on my understanding of supply and demand economics, you should be changing your price model. What you choose to do with the understanding that the system is providing you is still going to land on someone's lap. So your ability to be creative, your ability to write, your ability to wrangle concept and insight. What do you do with the information that you're being provided from a system that is finding things that you might not otherwise be able to find." –Joey Camire "How can we consistently use that [hyper-personalization] in our messaging without compromising our brand? And so the way that we succeed in doing that is really being super emotional and human.

Yusuke Goto would have crashed his Toyota Crown five years ago if the premium sedan hadn't detected the 45-year-old was veering off and righted the steering wheel. So when he found similar features in the cheaper Nissan Serena minivan, it was an easy choice. "Earlier you could only find such features in premium sedans but I have a big family and I want to make sure they are safe when we drive for a weekend getaway," Goto said on a recent visit to a Nissan showroom in Tokyo with his family. "I've become hugely interested in autonomous driving after that experience." At ¥3 million ($27,400), Nissan Motor's Serena minivan was the first model in Japan in its price bracket that offers what's known in the industry as "level 2" autonomous driving features, similar to what Tesla offers with its Autopilot function in the $80,000 Model S. A car with level 2 functionality can control steering and speed simultaneously without intervention for a short period, allowing the driver to take his hands off the wheel and foot off pedal at the same time. Nissan chose to debut its autonomous features in its mass-market model instead of the premium Infiniti brand, and the decision has paid off for the automaker.

Japanese auto manufacturer Nissan Motor Co unveiled to the media on July 12 its new self driving technology'ProPilot,' which assists the driver in maintaining speed during heavy traffic, changing lanes, and other driving maneuvers on a highway. Nissan's 2016 model Serena will be the first model to have the ProPilot technology, which is made possible by relatively simple technologies including a front-facing camera, an electronic parking break, and a 7-inch display monitor inside the car. "As part of the first step in (pursuing) our self driving technology called ProPilot, we are implementing the system into Serena. This is part of the company's roadmap, which in 2016 we begin implementing ProPilot (technology) that works within a single lane on highways," Nissan's Manager of Advanced driver assistance systems engineering department, Kiwamu Aoyanagi, said. By 2018, Nissan aims to have ProPilot be able to operate multiple lane changes, and by 2020, to be able to turn left and right at intersections.