Context, language, and reasoning in ai_ three key challenges market risk example

Does all that mean AI is finally here for non-vision applications? We believe the answer is an emphatic “yes”—but not with the current approaches used by IBM and Google.

Because of AI’s revolutionary potential, its applications in non-vision problems have attracted tremendous interest. Market risk reporting There have also been attempts to replicate what worked with spatial data and apply it to text (and numbers). Risk in stock market I’m referring to what seems like a blind rush of computational, statistically based approaches to process natural language. Var market risk Such approaches attempt to turn text into data and then look for deep patterns in that data.

That situation reminds me of when physicists entered the financial market space and attempted to create predictive models for financial data.

Market risk in banks ppt Such efforts are bound to fail, as has already happened to several companies. Hotel risk management jobs Eventually, the hype and illusion of applicability will wear off. Market risk analyst jobs south africa Then we’ll address the problem by focusing on the fundamental characteristics of the data and devising an approach that is more conceptually sound.­

AI technologies must overcome three challenges to be successful in the non-vision world (and perhaps even in the vision world): language, context, and reasoning.

A recent MIT Technology Review article, “ AI’s Language Problem,” eloquently points out the first challenge. Equity risk premium calculator Today’s AI technologies, including those IBM Watson and Google AlphaGo, struggle to process language the way that humans do. Managing market risk That’s because the large majority of the current implementations approach text as data, not as language. Market risk ppt They apply the same techniques that worked on spatial data to text.

The second challenge—understanding context—is related to the language problem, but is sufficiently significant that I think of it as an independent issue. What is the market risk premium Natural language text needs to be processed in the right context. Risk of stock market The right context can only be developed if the technology focuses on the language structure, not just on the words in the text, as most current technologies seem to be doing, according to a 2014 article in IEEE Computational Intelligence Magazine. Weather risk management jobs Then there’s the third challenge: the traceability of reasoning that the solution deploys to reach its conclusion.

Various technologies are attempting to address all three challenges today. Market risk policy Several successful enterprise AI solutions deal with language, context, and reasoning transparency effectively.

Current methods for natural language processing (NLP) are largely driven by computational statistics. Market risk management in banks pdf These methods don’t attempt to understand the text, but instead convert the text into data, then attempt to learn from patterns in that data. Market risk analysis pdf In the conversion process, we lose all context and meaning in the text. Australia market risk premium 2015 The assumption behind such approaches is clearly that, given sufficiently large collections of text, all possible permutations and combinations of meaning must be present. Market risk pdf Thus, discovering word-based patterns should reveal the intelligence in the text, which can then be acted upon. Market risk management policy Unfortunately, that outcome doesn’t occur in most real-world situations.

To address the language challenge in AI, we have to move from mechanically converting natural language to data through, for example, word occurrence-based logic. What is market risk premium We can then understand the language by using its linguistic structure and the principles we have learned to express our thoughts. What does a market risk analyst do I view this as moving from NLP to Natural Language Understanding (NLU). Current market risk premium s&p 500 In my view, NLP has come to symbolize the mechanical approach to natural language through conversion of text into data. Australian market risk premium Our real goal in AI is devising mechanisms for understanding the meaning of the written text.

A deep understanding of the linguistics structure in text would involve applying several principles from computational linguistics to decompose the text back into the concepts and verbiage used to connect them in the text. Market risk premium This is essentially reverse-engineering the text back to its fundamental ideas to understand how those ideas were connected together to form sentences and paragraphs.

RAGE AI has demonstrated such deep linguistic learning, and RAGE Frameworks has used this method to create and successfully deploy several AI applications in global corporations.

First, in many languages, certain words can be used in multiple senses. How to reduce market risk That makes it important to eliminate the ambiguity of all such words so that their usage in a particular document can be accurately understood. What is current market risk premium Word-sense disambiguation is an ongoing issue in linguistics, but researchers have made significant progress toward addressing it.

Second, text documents often use domain-specific discourse models such as legal contracts, news articles, research reports, and the like. Market risk management pdf Certain properties of such domain discourse models should be incorporated into the AI technology to enhance NLU.

Third, we use many words as proxies in the document for other concepts. Market risk premium 2016 For example, most commonly, we say “Xerox” for “copy,” “FedEx” for “overnight courier,” and so on. Risk of stock market crash AI technology must be able to recognize and understand these proxies.

Finally, the document may refer to knowledge that isn’t explicitly included of the text. Market risk premium today We can understand it only if we have that prior knowledge.

AI has to create a repository of such global knowledge that can be retrieved, in context, to supplement the text in the document to gain full understanding of the meaning of the text. Market risk example The Automated Knowledge Discoverer in RAGE AI is one such example of this idea, as I explain in more depth in my recent book, The Intelligent Enterprise in the Era of Big Data (John Wiley & Sons, 2016). Credit risk market risk This technology can automatically discover ideas related to a notion and expressions with various rhetorical relationships to the concept of interest.

For a period of time, such knowledge and global context may have to be refined by human experts. What is market risk But in a short period, we have found it possible to create enough knowledge in the machine for it to perform at better than 90 percent recall. Emerging market risk For instance, we created an AI application to categorize relevant content for a global consulting company across 20 of its practice areas. Market risk premium nyu The idea was to provide, on a real-time basis, distilled knowledge to all of its consultants, using information gleaned from each practice area. Market risk premium 2012 Automated knowledge discovery was used to expand this to a more global understanding. What is the current market risk premium Now, this application categorizes 40 million articles a month with greater than 90 percent accuracy through deep linguistic learning.

The final challenge we need to recognize is the visibility to the reasoning deployed by AI technology. Market risk analyst Almost all AI technologies using computational statistics are black boxes. Beta market risk There’s nothing wrong with that per se—except that when we get a recommendation from the AI technology and it isn’t intuitive, we have no way of understanding it. Risk analysis in stock market We also don’t know if it is truly causal or spurious. Market risk premium calculator We just have to blindly trust it.

Of course, there are applications where such visibility may not matter. What is the market risk premium today For instance, in the example involving the game Go, it wasn’t important to understand the reasoning deployed by the machine for its moves. Market risk premium s&p 500 Another example: While we’d all prefer for Internet searches to be more relevant, the false positives don’t bother us too much.

On the other hand, we believe that for many applications, such visibility will be essential for adoption. Market risk factors In certain mission-critical applications where people are held accountable—such as medicine and business—users have to develop trust that the engine’s reasoning is sound. Stock market risk Visibility would also make it easier to improve the engine in the event of false positives or false negatives. Global risks 2016 With a black box, we have to find enough instances of false positives or false negatives to rebuild the black box. Ibbotson market risk premium 2015 We’ll have no way of knowing whether all variations or permutations of that error have been addressed.

Venkat Srinivasan is the founding CEO of RAGE Frameworks and a successful serial entrepreneur. Current market risk premium 2016 He is also a former associate professor in the College of Business Administration at Northeastern University in Boston. Market risk premium australia 2015 He has published more than 30 articles in prestigious peer-reviewed journals and contributed to news publications such as The Wall Street Journal. Stock market risk and return He holds five patents in the area of knowledge-based automation and linguistics. Equity risk premium historical data He is the author of The Intelligent Enterprise in the Era of Big Data (John Wiley & Sons, 2016).