Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system has a sense making capability. Existing benchmarks measures commonsense knowledge indirectly and without explanation. In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. In addition, a system is asked to identify the most crucial reason why a statement does not make sense. We evaluate models trained over large-scale language modeling tasks as well as human performance, showing that there are different challenges for system sense making.

YOKOHAMA - An automated train operated by Yokohama Seaside Line Co. on Saturday traveled in the wrong direction, causing about 20 people to be injured, a local fire department said. Some appeared to have suffered serious but non-life-threatening injuries as the train made contact with a buffer stop at Shin-Sugita Station, the department said, but other details were not immediately available. The trains are on an automated guideway transit system connecting Shin-Sugita and Kanazawa Hakkei in Yokohama.

If you think you've got a bad case of the travel bug, get this: Dr. John Halamka travels 400,000 miles a year. Halamka is chief information officer at Harvard's Beth Israel Deaconess Medical Center, a professor at Harvard Medical School, and a practicing emergency physician. In a talk at Singularity University's Exponential Medicine last week, Halamka shared what he sees as the biggest healthcare problems the world is facing, and the most promising technological solutions from a systems perspective. "In traveling 400,000 miles you get to see lots of different cultures and lots of different people," he said. "And the problems are really the same all over the world. Maybe the cultural context is different or the infrastructure is different, but the problems are very similar."

Caris Life Sciences, a leading innovator in molecular science focused on fulfilling the promise of precision medicine, today presented a poster demonstrating how its advanced machine learning approach, Caris Next Generation Profiling, enables a proprietary algorithm to molecularly classify tumor samples into cancer types. These results, presented at the 2019 American Society of Clinical Oncology (ASCO) Annual Meeting, showcase how analysis of large combined molecular and clinical datasets can improve diagnosis of challenging cases, which is expected to inform increasingly personalized and precise cancer treatments. The poster, "Machine Learning Algorithm Analysis using a Commercial 592-gene NGS Panel to Accurately Predict Tumor Lineage for Carcinoma of Unknown Primary (CUP)," was presented at this morning's Developmental Therapeutics and Tumor Biology (Nonimmuno) Poster Session. Caris scientists detailed how Caris Next Generation Profiling identified molecular classifications for tumor samples with over 95% accuracy using next generation sequencing (NGS) data from 55,780 tumor patients. It generated an unequivocal result in the vast majority of cases of carcinoma of unknown primary (CUP), when there was ambiguity about tissue of origin.

BENGALURU: The market size in India for the MNC tech centre -- also known as global capability centre (GCC) -- touched $28.3 billion in 2018-19, compared to $19.5 billion in 2014-15, says a study by Nasscom and consulting firm Zinnov. That's a compounded annual growth rate (CAGR) of nearly 10% -- faster than that of the IT services sector during this period. India has by far the biggest presence of GCCs in the world (the segment's size has been estimated by multiplying the number of employees with the average cost of the employee). GCCs now employ more than 1 million people -- up from 7.5 lakh in 2014-15. They account for a quarter of the total workforce in the country's tech sector.

Artificial Intelligence (AI) technology has the capability to be one of, if not the, most impactful technologies ever and at the moment, like almost every other government on the planet, the US government has no idea how to properly regulate it. But what the US does know is that it doesn't want other countries using its own AI technology against it, especially in the event of war as we continue to see the emergence of autonomous AI powered weapons systems, from Chinese "fire and forget" cruise missiles to fully autonomous Russian nuclear submarines. As a result a new proposal published recently by the Department of Commerce lists a wide range of AI technologies that could potentially require a license to sell to certain countries, and the categories of restricted "exports" are as diverse as Machine Vision and Natural Language Processing tech. As you'd expect though it also lists military specific products like adaptive camouflage and surveillance technology. The small number of countries these regulations would target includes one of the biggest names in AI – China, who last year announced that they want to be world leaders in AI by 2030.

Russian President Vladimir Putin has offered the best insight yet at what shape the country's AI strategy will take. Putin ordered his government apparatus on February 27th to formulate a national artificial intelligence strategy by June 25th. With that date quickly approaching, the world is waiting to see Russia's AI plans. Back in September 2017, Putin famously said the nation which leads in AI "will become the ruler of the world." Understandably, Putin's comments generated fear of a cold war-like rush to militarise AI technology.

The insurance industry, no stranger to gauging risk, is facing one of its most profound disruptions in decades. Artificial intelligence, machine learning, Internet of Things, blockchain, data analytics and other emerging technologies are enabling many startups to nip at parts of their businesses. Incumbent insurers still have the advantage of institutional knowledge and regulatory expertise, as well as robust cash flows. But they can't sit still. Recognizing the technological winds of change, Reinsurance Group of America (RGA), one of the largest global reinsurers, created RGAX in 2015 to incubate and launch new products and services as its insurer clients seek to maintain their competitive advantages. RGAX CEO Dennis Barnes recently spoke to Knowledge@Wharton about the opportunities and roadblocks to digital transformation. An edited transcript of the conversation follows.

The deployment of machine learning models is the process for making your models available in production environments, where they can provide predictions to other software systems. It is only once models are deployed to production that they start adding value, making deployment a crucial step. However, there is complexity in the deployment of machine learning models. This post aims to at the very least make you aware of where this complexity comes from, and I'm also hoping it will provide you with useful tools and heuristics to combat this complexity. If it's code, step-by-step tutorials and example projects you are looking for, you might be interested in the Udemy Course "Deployment of Machine Learning Models".

Last year Kate Crawford, a New York University professor who runs an artificial intelligence research centre, set out to study the "black box" of processes that exist around the hugely popular Amazon Echo device. Crawford did not do what you might expect when approaching AI – namely, study algorithms, computing systems and suchlike. Instead, she teamed up with Vladan Joler, a Serbian academic, to map the supply chains, raw materials, data and labour that underpin Alexa, the AI agent that Echo's users talk to. It was a daunting process – so much so that Joler and Crawford admit that their map, Anatomy of an AI System, is just a first step. The results are both chilling and challenging.