How did people find answers before the internet?
Even those of us old enough to remember a life pre-web struggle to recall how we did our homework, checked for correct spellings or even resisted the urge to ask those questions we wouldn’t dare ask aloud without the relative safety of a search engine like Google.
And yet humans somehow managed to exist before the World Wide Web was created in 1989.
“I would like to thank my teammates, our coaches and of course, my parents. But most of all I want to thank our AI-powered robotic coach for making us the champions we are today.”
OK, perhaps this acceptance speech might not take place at next year’s Super Bowl LII. But AI’s impact on sport is already considerable and will continue to grow as franchises seek game changing advantages which will push them over the top against their opponents.
When it comes to chatbots, businesses want to know one thing. The million dollar question for a market which will be worth billions within a few years is – can my virtual agent answer my customers’ questions?
Assuming your chatbot has robust natural language processing (NLP technology), the most effective way to do this is through decision trees.
What is a decision tree exactly?
In the context of chatbots, a decision tree essentially helps them find the exact answer to your question.
Send your AI, machine learning, and predictive modeling questions to DRInsurance@datarobot.com
Every year I look forward to Gartner’s North American analytics conference. There are always some inspiring keynote speakers, a bunch of informative sessions to attend, and multiple opportunities to reconnect with industry colleagues. And, there always seems to be some annual underlying theme that Gartner wants attendees to take away.
The Gartner Data & Analytics Summit starts next week (March 5-8 in Grapevine, TX), and I’m looking forward to hearing Gartner’s perspective on the key trends in our industry, meeting with customers and prospects, and re-connecting with industry colleagues over some great Texas barbecue.
Today’s predictive analytics projects require a lot of collaboration and teamwork in order to grow and succeed. However, limited resources — such as not having enough available and skilled data scientists and tools that don’t offer high levels of automation — are hampering even the best teams out there. This is the moment when partnerships and collaborations step in to resolve these limitations as companies join forces to provide the resources needed to ensure success.
MIRI senior researcher Eliezer Yudkowsky was recently invited to be a guest on Sam Harris’ “Waking Up” podcast. Sam is a neuroscientist and popular author who writes on topics related to philosophy, religion, and public discourse.
The following is a complete transcript of Sam and Eliezer’s conversation, AI: Racing Toward the Brink.
1. Intelligence and generality — 0:05:26
2. Orthogonal capabilities and goals in AI — 0:25:21
3. Cognitive uncontainability and instrumental convergence — 0:53:39
New at IAFF: An Untrollable Mathematician
New at AI Impacts: 2015 FLOPS Prices
We presented “Incorrigibility in the CIRL Framework” at the AAAI/ACM Conference on AI, Ethics, and Society.
From MIRI researcher Scott Garrabrant: Sources of Intuitions and Data on AGI
News and links
In “Adversarial Spheres,” Gilmer et al. investigate the tradeoff between test error and vulnerability to adversarial perturbations in many-dimensional spaces.
Recent posts on Less Wrong: Critch on “Taking AI Risk Seriously” and Ben Pace’s background model for assessing AI x-risk plans.