Since 1950, when Alan Turing proposed what has since come to be called the Turing test, the ability of a machine to pass this test has established itself as the primary hallmark of general AI. To pass the test, a machine would have to be able to engage in dialogue in such a way that a human interrogator could not distinguish its behaviour from that of a human being. AI researchers have attempted to build machines that could meet this requirement, but they have so far failed. To pass the test, a machine would have to meet two conditions: (i) react appropriately to the variance in human dialogue and (ii) display a human-like personality and intentions. We argue, first, that it is for mathematical reasons impossible to program a machine which can master the enormously complex and constantly evolving pattern of variance which human dialogues contain. And second, that we do not know how to make machines that possess personality and intentions of the sort we find in humans. Since a Turing machine cannot master human dialogue behaviour, we conclude that a Turing machine also cannot possess what is called ``general'' Artificial Intelligence. We do, however, acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called ``narrow'' AI can still be of considerable utility.

We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions. The approach is especially suitable for domains that require reasoning over a diverse set of linguistic constructs but have limited training data. To address these challenges, we present the first system, to the best of our knowledge, that reasons over a wide range of semantic abstractions of the text, which are derived using off-the-shelf, general-purpose, pre-trained natural language modules such as semantic role labelers, coreference resolvers, and dependency parsers. Representing multiple abstractions as a family of graphs, we translate question answering (QA) into a search for an optimal subgraph that satisfies certain global and local properties. This formulation generalizes several prior structured QA systems. Our system, SEMANTICILP, demonstrates strong performance on two domains simultaneously. In particular, on a collection of challenging science QA datasets, it outperforms various state-of-the-art approaches, including neural models, broad coverage information retrieval, and specialized techniques using structured knowledge bases, by 2%-6%.

Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be difficult because the possible combinations of semantic inputs grow exponentially with the number of domains. To alleviate such scalability issue, we exploit the structure of dialog acts to build a multi-layer hierarchical graph, where each act is represented as a root-to-leaf route on the graph. Then, we incorporate such graph structure prior as an inductive bias to build a hierarchical disentangled self-attention network, where we disentangle attention heads to model designated nodes on the dialog act graph. By activating different (disentangled) heads at each layer, combinatorially many dialog act semantics can be modeled to control the neural response generation. On the large-scale Multi-Domain-WOZ dataset, our model can yield a significant improvement over the baselines on various automatic and human evaluation metrics.

Image-processing algorithms to smartly identify, caption and moderate your pictures. Convert spoken audio into text, use voice for verification, or add speaker recognition to your app. Allow your apps to process natural language with pre-built scripts, evaluate sentiment and learn how to recognize what users want. Map complex information and data in order to solve tasks such as intelligent recommendations and semantic search. Add Google or Bing Search APIs to your apps and harness the ability to comb billions of webpages, images, videos, news and much more.

The growing use of artificial intelligence in sensitive areas, including for hiring, criminal justice, and healthcare, has stirred a debate about bias and fairness. Yet human decision making in these and other domains can also be flawed, shaped by individual and societal biases that are often unconscious. Will AI's decisions be less biased than human ones? Or will AI make these problems worse? Will AI's decisions be less biased than human ones?

Why are coal plants in the U.S. and Europe closing at an accelerating rate, while in Asia, coal consumption went up and helped fuel an overall 1.7% year-on-year increase in global carbon emissions? Part of the reason coal continues to grow in countries like China and India is that in these areas, unlike in the U.S., emissions data can be shoddy or hard to acquire. Without accurate information it is harder to hold facilities accountable and keep them in line with meeting emission reduction targets. To address this situation, we are partnering with WattTime and the World Resources Institute (WRI), to launch a new project which will use satellite imagery to quantify carbon emissions from every major power plant across the world. This effort is being funded as one of 20 projects in the Google AI Impact Challenge.

With more access to data and growing computing power, artificial intelligence (AI) is becoming increasingly powerful. But for it to be effective and meaningful, we must embrace people-first artificial intelligence strategies, according to Soumitra Dutta, professor of operations, technology, and information management at the Cornell SC Johnson College of Business. "There has to be a human agency-first kind of principle that lets people feel empowered about how to make decisions and how to use AI systems to support their decision-making," notes Dutta. Knowledge@Wharton interviewed him at a recent conference on artificial intelligence and machine learning in the financial industry, organized in New York City by the SWIFT Institute in collaboration with Cornell's SC Johnson College of Business. In this conversation, Dutta discusses some myths around AI, what it means to have a people-first artificial intelligence strategy, why it is important, and how we can overcome the challenges in realizing this vision. An edited transcript of the conversation follows. Knowledge@Wharton: What are some of the biggest myths about AI, especially as they relate to financial services?

FUKUOKA: International Monetary Fund Managing Director Christine Lagarde warned on Saturday that the increasing presence of technology giants using big data and artificial intelligence could cause a significant disruption to the world's financial system. The rapid development of financial technology (fintech) has increased access to cheap payment and settlement systems for low-income households in emerging countries where traditional banking networks are scarce. But it has raised concern about the increasing dominance of big technology firms in mobile payments, which could force global policymakers to rethink the way they regulate the banking system and ensure financial settlements are executed safely. "A significant disruption to the financial landscape is likely to come from the big tech firms, who will use their enormous customer bases and deep pockets to offer financial products based on big data and artificial intelligence," Lagarde told a symposium on financial technology held on the sidelines of the G20 finance leaders' meeting in Fukuoka, southern Japan. While such innovation may help modernize financial markets, they could make the financial system vulnerable such by putting payment and settlement systems under the control of a handful of technology giants, she added.

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements -- instantly. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

The spread of tuberculosis (TB) has diminished in the developed world, but it is still prevalent in the developing parts of the world such as in Asia and Africa. The rise of HIV in the 1980's also saw an increase in TB infections due to the weakened immune systems of patients with HIV. Currently about 1.6 million people die from TB each year, and 10 million people develop active TB infections, which is also contagious. Tuberculosis is caused by Mycobacterium tuberculosis bacteria and it generally affects the lungs. Individuals can harbor the TB bacteria but show no symptoms.