Experimental methods for estimating the impacts of text on human evaluation have been widely used in the social sciences. However, researchers in experimental settings are usually limited to testing a small number of pre-specified text treatments. While efforts to mine unstructured texts for features that causally affect outcomes have been ongoing in recent years, these models have primarily focused on the topics or specific words of text, which may not always be the mechanism of the effect. We connect these efforts with NLP interpretability techniques and present a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. When used in an experimental setting, this method can identify text treatments and their effects under certain assumptions. We apply the method to two datasets. The first enables direct validation of the model's ability to detect phrases known to cause the outcome. The second demonstrates its ability to flexibly discover text treatments with varying textual structures. In both cases, the model learns a greater variety of text treatments compared to benchmark methods, and these text features quantitatively meet or exceed the ability of benchmark methods to predict the outcome.

Over the past few weeks, there's been some very public hand-wringing about artificial intelligence--a lot of it coming from people who have made A.I. their life's work. Geoffrey Hinton, dubbed the "godfather of A.I.," recently left his job at Google to embark upon a sort of media tour warning about the dangers of the technology. There was a public letter from Elon Musk and others calling for a pause in A.I. development and an essay in Time from theorist Eliezer Yudkowsky saying generative A.I. can harm humanity--or even end it. On Friday's episode of What Next: TBD, I spoke with Meredith Whittaker, president of the Signal Foundation and co-founder of the AI Now Institute at NYU, to sort through the real threat of A.I. and what the doomerism discourse is missing. Our conversation has been edited and condensed for clarity. What do you make of the concerns raised by Geoffrey Hinton and others when it comes to A.I. safety?

The industrial products industry is awash with data. Instrumentation, sensors, machinery, automation systems, production and operation, maintenance records, and health and safety applications collectively produce a constant flow of data. Industrial products enterprises need technology that supports the vertical delivery of insightful data throughout the organization, both to meet consumer needs and to aim for continuous process improvement. To address operating and market concerns – and deliver on the promise of Industry 4.0 – a small group of financial outperformers is using artificial intelligence (AI)/cognitive to do things differently. Here, they share their AI successes.

Artificial intelligence (AI) is still in its infancy, but it is truly a transformational technology. Thus far, mainly the companies with the largest stores of data have been able to benefit from the science. When talking about AI, most are referring to machine learning and, more specifically, deep learning, a technique that requires the processing of massive amounts of data in order to train these systems. So far these innovations haven't been largely available to smaller companies, but that is changing. The widespread adoption of cloud computing is beginning to alter that dynamic, as companies are working to incorporate these capabilities into their cloud offerings.

I finally beat the S&P 500 by 10%. This might not sound like much but when we're dealing with large amounts of capital and with good liquidity, the profits are pretty sweet for a hedge fund. More aggressive approaches have resulted in much higher returns. It all started after I read a paper by Gur Huberman titled "Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar," (with Tomer Regev, Journal of Finance, February 2001, Vol. "A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed's stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday. It closed above 30 in the three following weeks. The enthusiasm spilled over to other biotechnology stocks. The potential breakthrough in cancer research already had been reported, however, in the journal Nature, and in various popular newspapers including the Times! Thus, enthusiastic public attention induced a permanent rise in share prices, even though no genuinely new information had been presented."

I finally beat the S&P 500 by 10%. This might not sound like much but when we're dealing with large amounts of capital and with good liquidity, the profits are pretty sweet for a hedge fund. More aggressive approaches have resulted in much higher returns. It all started after I read a paper by Gur Huberman titled "Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar," (with Tomer Regev, Journal of Finance, February 2001, Vol. "A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed's stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday. It closed above 30 in the three following weeks. The enthusiasm spilled over to other biotechnology stocks. The potential breakthrough in cancer research already had been reported, however, in the journal Nature, and in various popular newspapers including the Times! Thus, enthusiastic public attention induced a permanent rise in share prices, even though no genuinely new information had been presented."

Future interactions with online customer services will make a drastic change as these communications may no longer involve humans but by chatbots powered by artificial intelligence (AI). Businesses are looking toward the best use of AI to process vast amounts of data. According to CNBC Technology, 2017 will be a year where investors will be looking towards AI-powered chatbots. Equity strategist Beijia Ma at BofA Merrill Lynch stated that big data is the input for things that AI is making people smarter about what to do with vast amounts of data and how it can improve goods and services. Amazon, dominates in this field as of the moment.