Evaluation of potential AGI systems and methods is difficult due to the breadth of the engineering goal. We have no methods for perfect evaluation of the end state, and instead measure performance on small tests designed to provide directional indication that we are approaching AGI. In this work we argue that AGI evaluation methods have been dominated by a design philosophy that uses our intuitions of what intelligence is to create synthetic tasks, that have performed poorly in the history of AI. Instead we argue for an alternative design philosophy focused on evaluating robust task execution that seeks to demonstrate AGI through competence. This perspective is developed from common practices in data science that are used to show that a system can be reliably deployed. We provide practical examples of what this would mean for AGI evaluation.

Think about your most personal ritualistic event--walking your dog, grocery shopping, or a weekly meet-up with your friends. Most likely, it takes place in the same place, close to home, with the same people, and at a certain frequency. Rituals occur also on the social level; for example, Sunday Mass, birthday parties, and sports competitions. Ritualistic events are characterized by two dimensions: continuity and repetition, and emotional engagement. In this post, we discuss the links between rituals and data science, arguing that thanks to these two characteristics, machine learning algorithms can model rituals relatively easily.

Google's AI fundamentals video- covers what AI is, use cases and the impact it's having on our world. Watch here Azure AI Fundamentals course- teaches the basics of machine learning services. Really useful for those with non-technical backgrounds to understand the power of AI, what it can do out of the box and the problems it can solve. Find out more here, scroll down to the learning path Python data science handbook- Python is the go to programming language for machine learning engineers. I recommend checking out chapters 2,3 and 4 to get familiar with Python from a data science perspective.

Sometimes it just takes a simple, provocative statement to kick-off the innovation process – to remove an everyday given like driving a car or possessing a landline phone or centralizing all of your data in the cloud – to fuel the innovation process. Henrik Christensen, director of University San Diego's Contractual Robotics Institute, issued such a provocative statement: "My own prediction is that kids born today will never get to drive a car." I have recently been promoted to Chief Innovation Officer at Hitachi Vantara. I am very excited about the opportunity to build upon my work to interweave data science, design thinking, value engineering and economics to create a "Pathway to Analytics-driven Innovation" map that helps organizations derive and drive new sources of customer, product and operational value. Think of the "Pathway to Analytics-driven Innovation" as a maturity model that measures how effective organizations are at leveraging analytics to deliver innovative products and services to the market.

We study homeless service provision in the United States from a data science perspective, with the goal of informing homelessness prevention efforts. We use machine learning techniques to predict household reentry into a homeless system using an administrative dataset containing both demographic and service information. This data recorded all publicly funded services provided in a Midwestern US community from 2007 through 2014. We find that several techniques can provide useful lift in the prediction task, with random forests achieving an AUC around 0.7. Prediction improves significantly when conducted within calendar years, compared to across years, suggesting that changing dynamics drive repeated need for homeless services. We also analyze key service usage patterns that are associated with lower probabilities for reentry. Counterintuitively, individuals receiving the least intensive services provided through the homelessness system exhibit significantly lower likelihoods for further system involvement compared to individuals who received more intensive services, even after accounting for initial differences through propensity score and nearest neighbor matching. These result provide intriguing insights into homelessness service delivery that need to be further probed. In particular, it is unclear whether these less intensive services sustainably address housing needs, or whether, in contrast, frustration with inadequate services drives clients away from the homelessness system. Our results provide a proof-of-concept for how data science approaches can drive interesting, socially important research in the provision of public services.