This article was written by Gregory Roberts for Bloomberg BNA. Entrepreneurs and innovators are invading the complex and often arcane world of regulatory compliance with some of the same technologies that have disrupted the core operations of the financial services industry. Machine learning, biometrics and the interpretation of social media and other unstructured data show promise as potential compliance tools for regulatory obligations that have mushroomed in the aftermath of the financial crisis, along with the investment and costs associated with them. Regtech is "the use of new technologies to solve regulatory and compliance requirements more effectively and efficiently," as the Institute for International Finance (IIF), a research-oriented trade association in Washington, put it in a March report. "I would define it as technological advancement that assists those focused on compliance and regulatory-related activities in their professions," Kari Larsen, counsel at Reed Smith LLP in New York, and formerly in the Enforcement Division of the Commodity Futures Trading Commission, told Bloomberg BNA.

An earlier version of this paper was presented on Oct. 21, 1984 as The Thoughts of Armchairs and Children at Middle Atlantic States Philosophy of Education Society Fall Conference, Rutgers University "Thinking" Like Computers Do 2004 Edward G. Rozycki, Ed. But what is then the difference between the internal speech of this armchair and that of another standing by it? How is it then with a human being? Where does she talk to herself? Why is it that this question seems senseless -- no specification of place is necessary except to say that the human is talking to herself? On the other hand the question as to where the armchair is talking to itself seems to demand such an answer. The reason is this: we want to know how the armchair is supposed to be like a human being, whether, for example, its head is the upper back of the chair, etc. What process is occurring when one talks to oneself inwardly?

You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem. One of the key themes for the event emerged from a survey of the attendees. Everybody who responded to the survey said that ML is either important or very important to their company and industry. However, more than half of the respondents said their organizations did not have adequate expertise in ML to be able to do what they need to do.

Vnomics Corp. is providing matching funds, data and expertise to support research at the University of Rochester into the use of machine learning to improve the fuel efficiency of commercial vehicles, according to the analytics company Rochester, New York-based Vnomics said it uses proprietary algorithms in True Fuel, its tablet-size analytics product, to provide feedback to drivers and fleets to help achieve optimal fuel economy. Machine learning is the method of combining statistical data, real-time analysis and algorithms to find patterns in data in order to deliver instant recommendations, according to the company. "While we already use machine-learning principles to determine a particular vehicle's potential fuel economy, our goal with this collaboration is to generate a new machine-learning model that can actually predict fuel consumption," said Lloyd Palum, chief technology officer at Vnomics. Vnomics' support is part of a grant awarded by the National Science Foundation for its Young Innovators Internship Program. The program's objective is to identify opportunities for early career researchers to interact with companies to collaboratively explore how to apply data science approaches to solve substantial industry challenges, according to the company.

Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before.

Artificial intelligence could scale new heights on its growing application in Southeast Asia, thanks to the region's relative openness to data collection. Bumrungrad International Hospital in Bangkok began using IBM's Watson AI platform to assist in cancer treatment late last year. The system automatically collects and learns from clinical studies and new papers, suggesting treatment options and giving expected success rates for each patient. Watson lets doctors make unbiased decisions on the best options, according to James Miser, Bumrungrad's chief medical information officer. The Thai hospital has consulted with Watson in treating breast, colon, lung and prostate cancer in about 50 patients so far and intends to expand its use.

Apple has bought Turi, a Seattle-based machine-learning startup. The purchase price was "around" 200 million, Geekwire reports, citing a source close to the deal. Turi was founded by a University of Washington professor, and it specialized in providing tools and frameworks for other developers to incorporate machine learning and artificial intelligence into their apps. For example, Turi's tools allow developers to build more advanced fraud detection into their applications. Apple has heavily invested in artificial intelligence in recent years, especially through purchases. Last fall, it bought Perceptio and VocalIQ.

Every startup business faces one crucial initial challenge: Capturing and retaining good employees. Without that key element, success can be very elusive. For Finance, Insurance, and Real Estate, for instance, only 58 % of new companies are still in business after four years. In Retail, only 48% are still on their feet after four years. And with Information startups, only 37% are still running after four years.

Google's DeepMind division has announced a partnership with the NHS's Moorfields Eye Hospital to apply machine learning to spot common eye diseases earlier. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, with the aim to speed up the complex and time-consuming process of analysing eye scans. The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Two million people are already living with sight loss in the UK, of whom around 360,000 are registered as blind or partially-sighted.

With the corporate world embracing digital technologies, organisations are increasingly looking for resources with a difference who could enable them to create a distinctive presence in the marketplace. They are keen that the resources they onboard have analytical skills, visioning skills, learning skills, innovation capabilities, social skills and problem solving skills at all levels in the organisation--not just at the top. This is because digital transformation is unleashing a virtuous change at a regular interval to the processes and interactions with various stakeholders at multiple levels within the organisation and within the industry and it is expected of employees to adapt and respond quickly to these changes. Hence in addition to reorienting the employees within the organisation towards the need for the skills mentioned above, the new generation employees organisations wish to employ are expected to have these competencies. The mindset and the capabilities required to be successful in the digital era have to be developed while the next generation is still part of the academic system.