Dialogue with the invisible Jeeves

by Anton Ivanov, Regional Development Director at Spitch AG

Dialogue between humans and machines is already a reality. But where is it leading us? Will technological developments eventually allow robo-advisors and personal assistants to take on almost-human characteristics? Artificial intelligence (AI) lies at the heart of the matter, but it is also about voice modulation and finding a voice-over that connects on an emotional level. There are not yet any clear answers to these questions; however, it is possible to predict what will influence the progress of technology and how the market is set to develop: namely, through the ability to place basic human needs and interests at the centre of the entire ecosystem – and serve them as effectively as possible in terms of cost, speed, and quality. It is evident that the driving force behind this process will be machine learning through real-time dialogues via a Voice User Interface (VUI).


What is this dialogue about?

An invisible and somewhat unobtrusive Jeeves responding to daily needs of a typical modern-day Wooster? The majority of forecasts focus on automating lifestyle services – whether these are basic financial transactions like moving savings to an account with a higher interest rate and balancing portfolio risk; ordering food and household goods, or using a VUI for the conversational programming of IoT (Internet of Things) devices. It is all about day-to-day needs, such as booking hotels and arranging meals out with friends, travelling, doing sports, using health services, planning and paying for education, insurance and so on. Even offering gift ideas and style advice about clothing are possibilities. Constant background analysis of personal choices and preferences, together with the smart use of statistics and big data (including unstructured data) delivers a higher cost/quality ratio than even the capabilities of the best human PA. It is likely that psychology of communication, sentiment analysis, and conversational excellence will be among the other methodologies that are applied to delivering first-class performance.


It begins with a simple voice-driven IVR (interactive voice response), whereby a customer of a telecommunications company or a bank does not have to wait on the line listening for all the complex menu options of an old-style DTMF IVR (DTMF, or dual tone multi frequency, is the tone signal to the phone company that you generate when you use the keypad of an ordinary telephone) – possibly even forgetting the first few options by the time they reach the last ones. Instead, the caller simply says what they want and machine deals with it. The machine has been trained specifically to understand all the permutations of ways to formulate, articulate, and voice the reasons for calling. Furthermore, it can also detect whether or not the caller is happy with the service.

Contextual understanding comes courtesy of speech recognition and analysis, semantic interpretation and emotion detection techniques, which makes such dialogues possible. Furthermore, voice biometrics take care of identifying and authenticating user identity, allowing the processing of sensitive personal data or financial information. It is anticipated that the next steps in the advancement of human-machine dialogue will be driven by new approaches that help AI emulate creative thinking and emotional intellect, and respond to complex feelings or psychological conditions.


What are the main obstacles in this?

Speech recognition accuracy is still not 100 percent satisfactory – to say nothing of “understanding” in the human sense. There has also been little progress in noise filtering, the general ability of systems to cope with acoustic environments that are unfamiliar to them, or indeed in the emulation of emotion in text-to-speech, among other things. On the basis of the situation currently, overcoming most of these hurdles will increasingly require innovative algorithms and sufficient data to facilitate machine learning. Technologies such as those of Spitch AG can deliver the highest accuracy if they are trained using massive amounts of customer audio data: in other words, data from the same very end-users who will later use the products being developed with those technologies.


Possible concerns:

  1. Can the AI of a machine harm humans?
  2. Could we harm ourselves by treating AI machines as slaves?


This invites a more philosophical question of whether artificial intelligence – if/when it is fully realised – is a form of semi-autonomous artificial life and personality; whether it would essentially compel us to follow a new type of ethics, or whether general moral conventions of human relationships would apply. For the foreseeable future, AI will remain fully dependent on human programming, and it will be barely capable of independent thought or action. Therefore, the only realistic manifestation of danger could result from basic system abuse or as a consequence of a human error, such as letting automation take control where it shouldn’t. The powerful image of “Skynet” terminating life on Earth, mismanaging your pension funds and deleting all your records is sufficiently disconcerting for us to consider threats that might arise only in the very distant future. The old notion of prevention being better than cure is both valid and important in this context.


Where is the money in this picture?

KPMG envisages an “invisible bank” for 2030:

“… There is no banking app – access to money is tightly interwoven with healthcare, time management, leisure and friendship. Visiting a bank will be as alien as telephoning via a landline. Banks will be just as invisible, but also as essential, as the manufacturers of 4G base stations are today”.

The KPMG vision for retail banking in 2030 is one of an unbundled industry, with three distinct components. The first layer – represented by universal personal assistants like EVA in the KPMG example – is the platform layer. Then come the product and process layers. This means that the banking industry will face a period of significant structural reform. At present, it is possible to see only incremental improvements in the customer experience through contactless payments, faster onboarding processes and so on. Genuine, transformational innovation is still rare.

However, KPMG notes,

“Customers are increasingly using other channels to fulfil functions that were previously dominated by banks. The arrival of services such as Apple Pay – to which most banks have signed up – hints at a future where financial brands are hidden behind devices… Bank brands remain highly trusted. Some would argue that they could develop lifestyle layers to compete in this platform space. This is one possible scenario…”

Other scenarios are dependent on the evolution of smartphones and other personal mobile devices, along with their ‘always-on’ voice user interfaces. These make it possible to access essential banking services, whilst robots and PAs surpass the scope of human capability as far as speed and the volumes of data they can process.

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