I turned myself into an AI-generated deathbot - here's what I found If a loved-one died tomorrow, would you want to keep talking to them? Not through memories or saved messages, but through artificial intelligence - a chatbot that uses their texts, emails and voice notes, to reply in their tone and style. A growing number of technology companies now offer such services as part of the digital afterlife industry, which is worth more than £100bn, with some people using it as a way to deal with their grief. Cardiff University's Dr Jenny Kidd has led research on so-called deathbots, published in the Cambridge University Press journal Memory, Mind and Media, and described the results as both fascinating and unsettling. Attempts to communicate with the dead are not new.

As artificial intelligence (AI) increasingly affects the performance of products consumers use every day, discussions about who owns the AI risk is likely to become more intricate. For companies making these products, it's important to know the risks involved, be they reputational, business-related or consumer risks. "Sometimes we're having to talk to our clients about the AI they use -- where they could be sued, for example, by a third party who interacts with it…or it could end up being a first-party loss because they're using it for themselves," said Kelly MacDonald, Aon's regional sales director and senior vice president, Commercial Risk Solutions. AI could fall under a host of liabilities, programs or policies, but many risks associated with AI are only partly insurable, says MacDonald. "It probably isn't going to all be picked up under one specific placement, it will be a combination," adds Katharine Hall, Aon's senior vice president and cyber practice leader of commercial risk solutions. "How does your general liability placement work with your tech E&O, work with your cyber?"

Your client is manufacturing a car that includes software to apply the brakes automatically when approaching slow traffic. No doubt, this software has some built-in artificial intelligence (AI) elements. When insuring a vehicle manufacturer, it's not too difficult to spot the potential for critical failure, especially one that could pose risks for consumers. But how does the AI itself come into play? How do insurers pin the risks?

How do researchers talk to one another about the ethics of our research? How do you tell someone you are concernened their work may do more harm than good for the world? If someone tells you your work may cause harm, how do you receive that feedback with an open mind, and really listen? I find myself lately on both sides of this dilemma--needing both to speak to others and listen myself more. It is not easy on either side.

Organizations failing to adopt AI risk falling behind in their markets. Even without the budget for it, advancing the AI know-how in your organization must include democratizing AI so it can become the province of everyday operations, and not just data scientists. "'Democratization' can be a dirty word," wrote blogger Chrissy Kidd in 2019. "Some folks hear it and conjure up long conference calls or work meetings abuzz with words like synchronicity and alignment. On the much more positive side, however, democratization is a good thing, where a resource that is useful can become more readily available to the masses."

VentureBeat recently interviewed five of the most intelligent, expert minds in the AI field and asked them to make their predictions for where AI is heading over the course of the year to come. Chintala, the creator of Pytorch, which is arguably the most popular machine learning framework at the moment, predicted that 2020 will see a greater need for neural network hardware accelerators and methods of boosting model training speeds. Chintala expected that the next couple of years will see an increased focus on how to use GPUs optimally and how compiling can be done automatically for new hardware. Beyond this, Chintala expected that the AI community will begin pursuing other methods of quantifying AI performance more aggressively, placing less importance on pure accuracy. Factors for consideration include things like the amount of energy needed to train a model, how AI can be used to build the sort of society we want, and how the output of a network can be intuitively explained to human operators.

AI is no longer poised to change the world someday; it's changing the world now. As we begin a new year and decade, VentureBeat turned to some of the keenest minds in AI to revisit progress made in 2019 and look ahead to how machine learning will mature in 2020. We spoke with PyTorch creator Soumith Chintala, University of California professor Celeste Kidd, Google AI chief Jeff Dean, Nvidia director of machine learning research Anima Anandkumar, and IBM Research director Dario Gil. Everyone always has predictions for the coming year, but these are people shaping the future today -- individuals with authority in the AI community who treasure scientific pursuit and whose records have earned them credibility. While some predict advances in subfields like semi-supervised learning and the neural symbolic approach, virtually all the ML luminaries VentureBeat spoke with agree that great strides were made in Transformer-based natural language models in 2019 and expect continued controversy over tech like facial recognition. They also want to see the AI field grow to value more than accuracy.

The Mabu robot, with its small yellow body and friendly expression, serves, literally, as the face of the care management startup Catalia Health. The most innovative part of the company's solution, however, lies behind Mabu's large blue eyes. Catalia Health's software incorporates expertise in psychology, artificial intelligence, and medical treatment plans to help patients manage their chronic conditions. The result is a sophisticated robot companion that uses daily conversations to give patients tips, medication reminders, and information on their condition while relaying relevant data to care providers. The information exchange can also take place on patients' mobile phones.

Slowly and silently, they glide across the floor wearing bright yellow dresses that look like they were plucked from a haunted 1920s boarding school. No, you haven't encountered some Mothman-like terror entombed inside a department store mannequin, the byproduct of a twisted, futuristic fever dream. You've merely stepped inside Mongkutwattana General Hospital in Bangkok, where a team of robot nurses has been unleashed to make life easier. Their job: ferrying documents between eight stations inside the health-care facility, a job that used to be carried out busy human nurses, hospital director Reintong Nanna told Newsflare last year. "These robotic nurses help to improve the efficiency and performance of working in the hospital," he said.

Cory Kidd, PhD, has spent two decades working at the intersection of healthcare and technology. Today, Dr. Kidd is CEO and Founder of Catalia Health, a company launching Mabu, a tablet built into a sculpted, stylized body that's designed to serve as a personal healthcare companion for patients at home. It reminds patients to take medications, asks how they are feeling, and contacts their caregiver if necessary. Catalia conducted a series of studies over two years using six different types of robots. One clinical trial involved 45 patients using the robot in their homes for two months.