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AI and its role in reshaping life skills as we know them. AI + Bot + Human = ? (whatever we can’t think of right now…) Credit bgr.comEarlier this year, I had the pleasure of addressing a large audience at a conference, presenting to a group of mixed professionals (including C-suite, Manager, Mid-level and executives, across a broad range of industry including IT, services govt, utilities, education, non-profit, hospital, health care, Financial services, insurance, automotive, Pharmaceuticals, aviation, aerospace, medical devices). The topic was, embracing and humanizing customer self-service channels; creating a personalized user experience in self-service channels.  I have provided an overview of the presentation here for your convenience. I implore you to ‘catch up’ and watch the link. At the conference I had the pleasure of meeting Mr. Rohit Mandana and we had a very interesting discussion about life outside of our professional engagements and then as these things do, our passion for our professions jumped back in and we were back and hot on the trail of bots, chatbots, AI and the role that they are playing in reshaping life as we know it. Sure there’s all the latest stuff like Google Home and Uber Driverless (although now suspended I believe this tech will proceed) but we’re beyond that now. So, the chat moved quickly to debate about all the benefits the world of AI serves for us, as humans. The discussion quickly turned to the most precious thing in our lives, our children. We discussed the application of AI and the impact it’ll have on future generations. One of the conference participants was sitting next to us and raised the question: “…would a man become stupid as a result of the bots and AI thinking and doing everything for us? Will our skill set become redundant?”. But it was at this point I raised quite the opposite. I explained how my children are learning at school about the Classical Core elements of water, fire, wind, air as proposed by Empedocles and I suggested: what if there were more elements which we, the human race, have not yet discovered. For example, AI in a laboratory environment is helping scientists push the boundaries on discoveries never possible without AI intervention. Introducing AI to human life makes an incredible combination when we conceive how much discovery can take place with AI on our side. Rohit shared a story introducing into my mind an exceptional concept. He told a story about a village in a third-world country, without specialist doctors. He said the medical pressures mounted greatly at a local clinic as one single doctor, a General Practitioner, was responsible for the health of this entire community. A team of AI developers in a western, first world country were experimenting with this General Practitioner by providing him with an artificial intelligence bot (via iPad application) which would enable this doctor to make specialist-like decisions in the absence of being one himself, in real time and enhancing the life span of this entire community, single handed.  As an example, a general practitioner may not be equipped with the formal qualification to perform many procedures traditionally. But, now an AI-based application could help him make an initial risk assessment to pre-qualify the risk level and only if compliant to acceptable medical thresholds, allow the general practitioner doctor, to proceed with the activity. After risk assessment, and with the help of the AI or App, they could make an assessment to perform the procedure or not. If a decision is made to proceed and obviously based on the initial risk assessment, the AI and App would next provide the direction needed to successfully perform the procedure. When allowed to percolate for awhile, this hybrid approach to the GP-AI-medical procedure, effectively renders the doctor with the new specialist-like skill, a potential lifesaver, extending the life span of the village community with future-tech, helping so much more patient cases. Not only does this example emphasize the power of a collaborative human and AI hybrid model but, it reinforces the ability for men to be forced into learning new skills thought about by introducing a catalyst, as represented in this example by an AI bot. In turn — we evolve further as a race. I was recently introduced to one of Elon Musks latest ventures, Neuaralink(AI smart-chips implanted into humans brains to enhance us……if this article has interested you, then check it out. Seriously scary stuff.  Image ref: greatestideaever.wordpress.com Just as similarly the Airtasker platform is creating new jobs unheard of in years past, so too does AI force the human hand to reshape its view on life skills.
Bots don’t have all the answers especially if humans haven’t input them into their databases. Humans fill this gap. It is up to us to embrace this beautiful AI technology, leverage it and evolve. My children sit in a classroom-style environment as schools have done for the past 200 years. I don’t agree with this. Isn’t it about time this changed? I think it’s about time for new jobs, the current world, even its tools (Google, Facebook) seem a bit stale and boring to me. Isn’t it about time for a change? Aristotle added a fifth element, aether; as it has been called Akasha in India and quintessence in Europe. In his novel ‘Think and grow rich’, Napoleon Hill expands on his view of the aether in human life. Many other philosophers and scientist have commented on the aether but with AI on their side, maybe humanity can get closer to solidifying the theories. This article was written by Glenn Miller. Please visit my Personal Website and feel free to connect. I love speaking at conferences and business forums, meeting new, innovative and exciting people. 
by Glenn MillerCreating a Personalised User Experience in Self Service Channels
Clariden Global (Singapore based international conference and exhibition production company) recently hosted me to speak at their International call centre conference.
My Bot, Chatbot and AI articles have been attracting quite a crowd and making some wonderful progress lately. As a result, I decided to incorporate their content and extend one of my usual Digital Marketing Strategy and Customer Experience stage presentations to include Customer Self Service and related Channels, featuring some of the hot topics I have been writing about. The presentation took place a couple weeks ago and it went phenomenally well. As always, I was humbled to share the stage with an amazing line-up of speakers, for example: Bianca Bowron-Cuthill (Head of Customer Care & Service, Australia at Intuit), Claire Hayes (Digital Operations & Transformation, Telstra and responsible for implementing their recent country-wide re-brand), Mitchell Mackey (Marketing Director of Ansell, Australia), Rohit Mandanna (Customer Centric Program Leader Office of Environment and Heritage, NSW Government) to mention a few. Many audience participants approached me after my presentation requesting a copy of the presentation and encouraging me to share my research and findings with my followers. In the good spirit, I decided to publish the presentation (produced on Prezi and featured below). I encourage sharing and I welcome feedback and comments, in kind.
This 'Embracing and Humanising Digital Self Service Channels' presentation features thought leadership in Customer Experience and Customer Self Service Channels and demonstrates why all companies should consider the impacts of AI, Bots and Chatbots on their Customers' Experience, their Businesses (Customer) Strategy, Customer Care policies, Customer Experience and digital self service channels in 2017 and beyond.
The presentation includes reference and explanation into Autonomous Transport, Driverless Vehicles, Passenger Flying Taxi Drones, Deep Learning, Natural Language Processing (NLP) and Artificial Intelligence (AI) powerhouses for example; Microsoft LUIS, IBM's Watson and more recently, Salesforce's Einstein. Like a bad situational joke, sometimes you need to be there, to fully experience the context behind the joke and to enjoy the situational context that leads to the laughter, that should come with it. If you feel like the presentation lacks context please get in touch with me, I’d be happy to complete the picture for you. Thanks for following, article written and presentation produced uniquely and originally by Glenn Miller (Director, Customer Strategy & Experience, Lupo Digital).  Are these ingredients the future of human success?Autonomous or driverless vehicles are a hot topic on the AI scene right now. Google, Volvo, Tesla, Uber… these are just some of the big names in the race to prove that driverless or autonomous vehicles are better and maybe even safer than human-driven vehicles. I was at a family event recently and two guests were chatting about the Artificial Intelligence (AI) component of driverless or autonomous vehicles and more specifically, how these vehicles are currently unable to detect human movement at high speed. One cited the example of a child stepping into the road whilst a vehicle was approaching at high speed. Some debate ensued about the width of lanes in the road (surrounding the vehicle) and the impact they have on the judgement of the driverless/autonomous vehicles. The discussion evolved to include the concept of ‘human thought’ and ‘human mental processing’, relative to that of the machines/AI, and by parallel, if the machines/AI would make the same ‘judgement calls’ as people do and if machines/AI were not currently capable of doing so, then when. One said: “AI will never be capable of ‘thinking’ as we humans do and in fact these vehicles are not ‘Artificial intelligence’ at all but mere algorithms, computer programming, and this is why the vehicles cannot adapt, they require programming”. The other guest suggested AI could learn and adapt from one satiation to the next thereby teaching itself how to better handle the situation, over time. The other corrected them stating that it is not possible. “AI is in no way comparable to the human mind, it cannot learn, AI is simply algorithmic by human design”, he said. I challenged both asking if they had seen the film Ex Machina (2015)? This film depicts the very essence of this discussion whereby, a young programmer is selected to participate in a ground-breaking experiment in synthetic intelligence by evaluating the human qualities of a breath-taking humanoid A.I. and the film opens the imagination of its viewer of the power behind possibilities of machine learning.(Ex Machina - film review courtesy IMDb.)  Image courtesy The Massie Twins There was some confusion between these two people due to a lack of deep understanding on the topic. AI, bots and chatbots and in turn through them, the Internet of Things (IoT), are slowly beginning to ingrain themselves in our daily lives, taking full responsibility of our lives (as is the case of driverless vehicles) and in some cases intimately listening or overseeing our every move for example: in the home, with Amazon's Echo (including Amazon Alexa the home automation PA) or in your phone, with Google Assistant or Siri…(and the list goes on). Wikipedia is always a good gauge of communities opinion into the meaning of things (whilst it tries to be the truth, it is not always the factual truth). So, and out of curiosity I Googled driverless vehicles. Wikipedia came up as it usually does but it was incredible to see that the latest update to Wikipedia’s definition of Autonomous Vehicles (aka Driverless Vehicles) was 28 Feb 2017. This implies that the definitions, as society are defining them (in this field) are literally evolving as I type.  It was then I realised the need to expand on some fundamental concepts, the key ingredients of AI and the core differences between them. AI, Machine Learning, NLP, and Deep Learning?Artificial Intelligence According to Merriam-Webster dictionary, artificial intelligence is defined as :
Oxford Dictionary define AI as: “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” To my mind, artificial intelligence is under continual exploration so the ‘theory’ is no more. As the simplicity of definition 2 suggests by Merriam-Webster, AI is no longer just a theory. Machines are now capable of and imitating intelligent human behaviour. Naturally and imitating their creators, current AI are built to evolve. Ultimately, man is looking to empower computers to approach reasoning (to problem solving) and conquer everything in life, just as we humans do. Version upon version, AI continues to develop all the time and the process is iterative and built upon improvement, learning from mistakes of the past, one model at a time. Learn, build, learn, grow and so the cycle continues, just like a baby becomes a child who becomes an adult. To make the concept that is AI as simple as possible we can examine the basic components that go into making an AI being. In other words, how does AI go from a bunch of technology things to actually doing stuff for us? I play around with and build bots and Chatbots. If you’ve missed my previous articles, you can catch up here. In context of Bots and Chatbots and for the purpose of this article, I’ll use the basic diagram below to conceptually explain the components. Form left to right, the diagram depicts a Facebook messenger bot or just as easily an independent purpose build bot placed on the homepage of a website and with the purpose of connecting with customers in a customer service capacity providing and AI-like customer service experience or response to customer enquiries. In the centre is the NLP and Deep learning are and to the right and below the connectors and storage, allowing the bot to access a store of all the info is will need and in order use it to learn and grow.  Machine Learning Where does the bots (or AI) intelligence (thought processing) come from, how does a machine learn? Man programs this into the architecture in the form of a vast array of algorithms to iteratively automate the process of learning. The computer uses the algorithms to assess actions taken and decision made and their impact over time and in turn, at the same time, the machine learns. Wikipedia has this to say about Machine Learning: “Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". So, if machines are capable of learning and we are programming them to ‘think’ and respond, this begs the question: how can they learn to interpret the natural tongue and vast array of human beings different languages and the slang that we humans speak, across the globe? In addition, how can they understand, make conversation and put the ‘human’ back into the conversation and in human language (not algorithmic slur), just like humans do? This is where NLP comes in. NLP - Natural Language Processing NLP - Natural Language Processing is the component that bridges the gap between human talk and computer programmed understanding (regardless of which human is talking, what language is spoken and the way in which, perhaps grammatically, they are talking). The NLP component allows the computer to interpret the vast and complicated human language, understand what's being said, process it all, reflect what is being required of it and effectively ‘talk back’, equally like humans do. Through the process, the machine has to understand all the lingo being used and develop or adapt with the ability to reply, not as a computer but rather a human-like computer. NLPs are advancing at exceptional rates and it is this human-like and natural, behavioural part of the experience, the NLP handles. Deep Learning
Deep learning is a deeper level and subset of machine learning whereby a machine uses mass amounts of data and highly complex algorithms to ‘learn’ and to simulate human-like decision-making. The learning area within an AI is under massive development and growth currently coining the term, deep learning. Deep learning is the deeper part of the AIs brain and the conduit to the NLP allowing the machine to learn from everything and improve upon itself for next time, just as humans would. For example, if you grab your common iPhone and ask Siri if she’s happy, she answers: “I’m happy, I hope you are too..”. If you ask her why or what makes her happy, her answers are nonsense — she is not capable of an adequate human-like response. I believe that Siri is not built with Deep Learning capability yet but I do believe the iPhone and AI part of the iPhone of the future will harness all of our voice activated engagements (through the life of the phone) and can and will assess and analyse all conversations and behaviours (as exhibited through our actions and words), learn our mood and feeling (as expressed through our speech) and in turn could respond why it was happy and, what could make it happy (just like a child learns happiness from listening to laughter). In other words, the computer could use the algorithms we provide mixed with our responses to learn ‘how’ a human behaves and responds and mimic doing so, all by itself and just as expected from the prefect AI machine. This is the essence of deep learning. There are two current well-known products in the market leading the charge on deep learning capabilities: Microsoft’s LUIS and IBM’s Watson (and no doubt many more are being developed as I type). A good example to demonstrate what NLP and Deep Learning working together represent has been put very well by the Director of Product Strategy, Retail solutions provider at IBM, regards Watson: “ When people ask how Watson is different than a search engine, I tell them to go on Google and type ‘anything that’s not an elephant.’ What do you get? Tons of pictures of elephants. But Watson knows those subtle differences. It understands that when feet and noses run, those are very different things”. This principal has differentiated LUIS and IBM’s Watson based on one major factor or difference: Cognitive. Cognitive-based systems build knowledge and learn, understand natural language, and reason and interact more naturally with human beings than traditional programmable systems. The term “reasoning” refers to how cognitive systems demonstrate insights that are very similar to those of humans. (Ref: IBM Institute for business value — Your cognitive future — part I). These areas of AI and the various components are all just the beginning. Man has been studying behaviour, engagement and now mechanics of the brain, since the dawn of time and since the start of our very existence. We have used our deep learnings from our research and exploration to develop AI. Imagine how many more ground-breaking components will be exposed and how fast this can and will take shape especially with the assistance of our AI friends, by or side. Imagine a world where, for example, we could forecast the need for a new vessel to hold food and drink. The AI could assist by running through permutations even we have not been capable of thinking of yet and then presenting us with the blueprint of how to produce such an economic vessel…the future will be filled with these augmented experiences and all thanks to the scientists of our time for developing todays AI, machine learning, NLPs and Deep Learning. I have a great passion for these new technologies but I appreciate most people do not. I hope you’ve enjoyed this article and a deeper insight and understanding into the DNA of AI and in simple terms grasping that using deep machine learning and a natural language processor, a (computer) being can become artificially intelligent. Article written by Glenn Miller. Please visit my Website and feel free to connect. I love speaking at conferences and business forums, meeting new, innovative and exciting people. |
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