With the current pandemic accelerating the revolution of AI in healthcare, where is the industry heading in the next 5-10 years? What are the key challenges and most exciting opportunities? To answer those questions, DeepLearning.AI and Stanford Institute for Human-Centered Artificial Intelligence (HAI) are proud to present our virtual event, Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng, at 10am PT on April 29. What's special about this event is that you get to decide what our speakers talk about. If you'd like to submit and upvote questions for our speakers, please sign up for the Q&A General access ticket.
AI and Content Marketing series, we'll explore the future of artificial intelligence and content marketing. The future of AI and content seems to shine with the eyeball-grabbing glow of personalization. What do we mean by personalization, exactly? Imagine there's a car accident Monday morning in your neighborhood. That's the level of personalization that people are excited about for the future of artificial intelligence and content.
Recently some in the Singularity community have admitted that "language is hard" as you can see in this attempt to explain why AI has not mastered translation yet. Michael Housman, a faculty member of Singularity University, explained that the ideal scenario for machine learning and artificial intelligence is something with fixed rules and a clear-cut measure of success or failure. He named chess as an obvious example and noted machines were able to beat the best human Go player. This happened faster than anyone anticipated because of the game's very clear rules and limited set of moves. Housman elaborated, "Language is almost the opposite of that. There aren't as clearly-cut and defined rules. The conversation can go in an infinite number of different directions. And then of course, you need labeled data. You need to tell the machine to do it right or wrong."
No one in Washington seems to know what the story is, or even where to set the dateline. Is it the culture war over masks, in the Florida sunshine? Is it the crisis along the southern border? CNN's prime-time viewership is down thirty-seven per cent, MSNBC's numbers are not much better, and even Fox's are in decline. The morning political-newsletter writers, and many of the rest of us, have been reduced to replaying the dramas of the Trump Administration (Why is John Boehner backing an Ohio congressman whom Trump opposes?) or even the Obama years (How much hold does Larry Summers have on the Democratic Party?). For a moment this week the story was whether one of the Bidens' German shepherds, Major, has a biting problem.
Artificial Intelligence has come of age. The "Age of WithTM," where humans and machines work together, is upon us. Our ability to connect, collaborate, and innovate is creating remarkable new possibilities for businesses and the society, at large. And though AI has become ubiquitous in many ways--guiding strategies, improving processes, shaping business models, rethinking customer experiences, and even finding cures--we are only scratching the surface of what it can do. The power of automation and AI lies in re-imagining the way we do things.
But these scenarios depend upon an unanswered question: are machines intelligent to begin with? Computers are essentially logic machines that process digital information. But in a recent paper entitled "The Emperor of Strong AI Has No Clothes," physicist Robert K. Logan in Toronto and Adriana Braga in Rio de Janeiro argue that the dream of a super intelligence has limits that its adherents choose to ignore. The things the Singularity will never get right amount to a long list, to quote the two researchers: "… curiosity, imagination, intuition, emotions, passion, desires, pleasure, aesthetics, joy, purpose, objectives, goals, telos, values, morality, experience, wisdom, judgment, and even humor." A clever programmer can figure out how to get a computer to answer human questions like "How is your mother feeling?", "What does chocolate taste like?", and "Don't you just love fresh snow?"
Kira Radinsky, co-founder and Chairman of Diagnostic Robotics, wants to make healthcare more affordable and accessible. The lessons learned from initial deployments of the startup's AI-based digital triage platform in Israel and the U.S. and the valuable experience gained during the Covid-19 pandemic, point to a future of better healthcare: Providing the right treatment at the right time in the most appropriate setting. At the Mayo Clinic, Diagnostic Robotics' triage platform suggests possible diagnoses and provides a risk score for each patient based on their answers to questions regarding their medical conditions. The Mayo Clinic's Dr. John Halamka calls it "Waze for healthcare," stressing its use as a navigation system, matching patients with the right healthcare resource at the hospital's emergency room or even before they arrive there. The State of Rhode Island has used Diagnostic Robotics' platform to help manage its response to the Covid-19 pandemic.
Meta Description: Artificial Intelligence is increasing in sophistication year on year. It will help to define the future of how humans live. Here we explore this future. The onward progression of technology is unstoppable. There are so many applications of Artificial Intelligence, in online gaming to building management and into the workplace.
In 1997, an AI beat a human world chess champion for the first time in history (it was IBM's Deep Blue playing Garry Kasparov). Fourteen years later, in 2011, IBM's Watson beat two winners of Jeopardy! In late 2017, DeepMind's AlphaZero reached superhuman levels of play in three board games (chess, go and shogi) in just 24 hours of self-learning without any human intervention, i.e. it just played itself. Some of the people who have played against it say that the creativity of its moves make it seem more like an alien that a computer program. But despite all that, in 2019 nobody has yet designed anything that can go into a strange kitchen and fry an egg. Are our machines truly intelligent? The fact is that today AI can solve ever more complex specific problems with a level of reliability and speed beyond our reach at an unbeatable cost, but it fails spectacularly in the face of any challenge for which it has not been programmed.