For over 10 years David has helped drive hundreds of thousands of customers to businesses and understands what it takes to manage the chaos and headache of customers across different departments, systems and platforms. From roles in product development, product management, working in enterprises and advising for businesses going to sale and exit, David’s DNA is understanding the customer. David currently serves as Chief Operating Officer and Growth at Woveon, ensuring teams can scale and respond back to all their customers.
We live in a time of plentiful choices. Making the most of it, many organisations or start-ups in globally are creating their impact, in the shortest possible time, by means of integrating multiple channels of customer service in their businesses. These channels include phone support, email and chat support, social media support, or even text support.
Nowadays, integration of multi-channel customer services is one of the most important factors for a business to consider, due to many reasons. By providing a range of options to the customer, businesses in globally are attracting and growing their loyal customer base significantly. They build high value by affecting the way in which people are making purchase decisions nowadays.
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In addition, offering abundant choices to your customers is a forerunner for a business to maintain its presence, meet the customer needs round the clock irrespective of their location, and empower customers to take the right decision. To assist you further, let’s have a detailed look at the concept of multi-channel customer service.
Business Efficiency through Multi Channel Integration
Multi channel customer service holds huge potential in delivering business efficiency and excellent customer experiences. The customer experience offered by integrating a multi channel strategy creates a more personalised, optimised and responsive outlook of a business in competitive and transforming industries.
The primary channels that attract the maximum investment of various small or big organisations are 83% online or self-service – digital, 62% mobile apps, 34% email and surprisingly, 57% of investment is focused on the phone or voice channel while 18% towards face to face channels.
This clearly emphasises on the potential that opens up for a business with the integration of multi channel customer service. Along with that, the level of exposure of the customer base to your business improves, as you cater to the needs of your customers by all means, and without falling short of their expectations.
Are you increasing the investment in multi channel customer service this year, to attain higher business value in your particular industry?
If you too, plan to join the forces of digital transformation and differentiate your business from the existing competitors, then you must make the most of these consumer channel preferences, and make only the right decisions, most importantly in the right direction, to optimise your customer service experience.
The top most channels utilised by Australian consumers, to avail customer service, are through phone conversation – 62%, via self-service website – 41% and in person – 45%.
The Prospective Channels of Customer Service and their Importance
Organisations that believe in delivering exceptional customer support, reach out to their consumers through all of those channels where their customers are present. Thus:
If your existing customer base includes avid user of emails, opt for an instant email support.
If they like to reach your business on Twitter, make this platform as your potential channel of customer support to access your customers efficiently.
In continuation of the above stated Fifth Quadrant research, keeping a strong grip on all the available channels of customer support will be the only key factor in differentiating your business and moving it along to a true market leader.
Email: This is undoubtedly the most non negotiable channel for all types of businesses. Almost 91% of consumers utilise an email service, everyday. This is the easiest means of building instant rapport with your customer base.
Social Media: Social networks are now the most excellent means of accessing your customer and to grow your business. Companies who use social networks as customer support channels have 15% lower churn rates than the ones who don’t.
Self-service Knowledge Base: As the name depicts, the self-service knowledge base is extremely useful to help your customers get to know you better, without being present for their assistance, through a live channel of customer support. You can deliver exceptional assistance 24/7 with just a small team.
Voice or live Chat:Phone support is old-fashioned but considered as the fastest means of communication between your business and the customer base. In fact, phone assistance accounts for almost 68% of the speediest interactions. Similarly, 44% of customers say that having a live chat support during an online purchase creates a trustworthy relationship with the service providers and is accounted for, as the top feature a website could offer.
Multi Channel Customer Service – A mean of seamless consumer experience
Your customer wants to be able to contact you with whatever device they hold in their hands and that is what your business needs to do – make itself accessible, by all possible means, for your customer’s satisfaction.
Integrate many digital platforms and provide seamless consumer experience through different channels. If you manage to reach out to your consumer base through numerous channels, your business will be providing fantastic experience to its customers, which is hard to give up.
Gain more loyalty and trust with your instant customer service. Stay responsive to your customers and effectively provide them with updated information and flawless support.
In today’s fast paced and transforming world, a business that shows itself invested in providing exceptional support to its customers, by making the most of the multi channel customer service, will go a long way in building long lasting relationships with its customers and successfully make its mark.
Artificial Intelligence is certainly not a new concept by any means. AI has increasingly begun to take over multiple aspects of business management, marketing and sales in the recent years, more specifically, lead generation for B2B marketing in the form of multiple lead generation software which is available online.
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Artificial intelligence and the lead generations software that utilize AI driven platforms and algorithms function by collecting and analyzing useful data, utilizing passive records of sales, marketing strategies and circumstances to improve customer relationship management, providing valuable insights into business decisions and helping to identify profitable investments from the unprofitable ones to increase ROI.
Today, there are endless cases of corporations utilizing AI for B2B marketing campaigns and sales purposes with a variety of lead generation software available in the market.
Top 5 Best Lead Generation Softwares utilizing Artificial Intelligence
Please note that every software functions the same way or utilizes the same AI algorithms to provide actionable data.
1. Growbots AI
Growbots AI is an automated lead generation tool which has over 200 million contacts in a self-updating database that are connected through a variety of social media platforms. This database has individual customer profiles of each of its contacts, their businesses, social preferences and needs.
As a lead generation software, the Growbot allows corporations to find ideal customer profiles that match their niche markets within minutes from that massive database. The requirements of a corporation’s target market are input into the search engine which, with the use of specific AI algorithms, generates a list of specific clients that have the true potential to translate into sales.
The corporation’s sale representatives can use that list and reach out to the potential customers that have a need for their niche products to promote their business and successfully sell their merchandise.
2. Growthbot by Hubspot
Taking machine learning in sales and marketing to another level, GrowthBot by Hubspot is a lead generation software that integrates Artificial intelligence to Customer Relationship Management (CRM) systems. The GrowthBot works by connecting machine learning processes to the various messaging applications widely used by businesses today such as Slack, SMS systems or Facebook Messenger. These messaging apps are integrated with the chatbot, allowing businesses to ask directed questions and receive answers to any and all queries related to drive leads to their business.
For example, if you have questions regarding the business’s website traffic and its fluctuation, or want to find a niche market to target for your company within the local areas or want to reach out to prospective clients, the GrowthBot does that for you and more.
3. Conversica
Conversica is another one of its kind marketing and sales software for increased lead generation that is widely being used today. This software is, in actuality, a highly intelligent digital assistant that is completely automated, and powered by an AI algorithm.
This algorithm is human like with superhuman powers in the sense that it engages in real-time conversations with each and every single possible lead your business might have. It does this in order to converse with them, gather necessary contact information and then analyze the interest of the lead to determine whether or not it will translate into a successful sale. The assistant is trained to alert a real-time sales representative who can close the deal positively, based on certain triggers.
4. Albert by Adgorithm
Albert by the artificial intelligence-driven marketing firm Adgorithms is the secret behind the recent skyrocketing sales of one of New York City’s dealerships of the motorcycle conglomerate Harley Davidson. Unlike other lead generation software, Albert works across the various social media platforms such as Google and Facebook to determine the outcomes of the marketing strategies currently in play. This helps it sift through what was working, from what wasn’t. Based on those results, by using quantifying algorithms, Albert then optimizes the marketing campaigns to attract more customers or leads.
Albert utilized the existing consumer data from the motorcycle dealership to isolate the behavior and characteristics of past successful sales and used it to target customers that resembled the previous narrative.
5. Alexandrabot
Utilizing Google Analytics as its Artificial intelligence base, Alexandrabot emerged from a small research project by a student named Alexandra which creates a persona and helps form conversations with users from all walks of life.
Alexandrabot takes into account the information across various platforms of a particular visitor to a company’s website, then analyzes it for its potential to be turned into having a great conversation with users. Future development includes presenting visitor metrics with customized reports of how engaged a visitor was with your business. Alexandra is aiming to make this bot the world’s largest intelligent conversational AI platforms.
There’s an industrial revolution under way in businesses across the world, and it is all about automation. Businesses are embracing machine learning and artificial intelligence to make better decisions automatically. And the reason for this revolution is the comparative strengths of humans and computers.
Computers are strongest at repetitive tasks, mathematics, data manipulation and parallel processing. So long as a task can be defined as a procedure, a computer can do that task over and over again, without getting tired, giving the same results each time. Computers can manipulate numbers and data in volume much faster than any human.
Several years ago I went back to university to do a masters degree, and after a 25 year break from university I was out of practice at mathematics. Imagine my excitement and relief when I discovered that now there is software that will do algebra and calculus for me! And computers can do more than one thing at a time. Have you ever tried to rub your belly and tap your head at the same time? I can’t do both actions simultaneously. But modern computer networks are powerful, able to routinely do dozens of different processes at once.
This does not mean that humans are obsolete. What humans are much more skilled than machines at are communication and engagement, context and general knowledge, creativity and empathy. When I have a frustrating problem, I want to talk to a human. Someone who will understand my exasperation, listen to my experience and make me feel valued as a customer, whilst also solving my problem for me. Humans are much better at common sense than computers, instantly recognizing when a decision doesn’t make sense. And humans can be creative. I recently heard music composed by a computer, and I’m sure that song won’t make it into the Top 40!
Customer Service
Recently I had a conversation with the manager of a call centre that dealt with hundreds of customer service issues each day. In order to ensure the quality of the service and advice, the call centre operators were given scripts and were commanded to follow those scripts without changing a word. The problem was that both staff and customers became frustrated. Staff felt bored and unchallenged, and customers with non-standard problems felt like they weren’t being heard. Staff turnover increased, and customer satisfaction levels dropped.
Customer Satisfaction
The manager then tested using chatbots to answer simpler questions from customers, freeing up the human operators to deal with non-standard enquiries. This was a situation where computers had a comparative advantage over humans. The call center processes were fully defined, operating at scale, and the scripted answers were correct. The results spoke for themselves. Computers were much better at helping with the repetitive enquiries, and humans were better at dealing with the unusual enquiries. Staff engagement increased, as did customer satisfaction.
This has implications for human resources and process innovation. Processes that require humans to do repetitive, well defined tasks can be replaced by artificial intelligence. This frees up staff to do what humans are best at:
asking the right questions,
applying common sense,
creating new solutions,
evangelising new ideas, and
generating sales and profit.
Let your humans be human. Free them from repetitive tasks. Change job descriptions to focus on human strengths, and hire people who best embody the comparative advantages of humans. Look for human processes that are well defined and repetitive, and enhance the process by introducing artificial intelligence. Some ways company have started to incorporate artificial intelligence and machine learning into their processes include:
There are even some companies out there that have started automating the automation, like DataRobot. Instead of hiring and training up a data scientists, the arcane process of building predictive models, once the sole domain of data scientists, can all be automated. The system automatically builds predictive models based on your data, freeing up your humans to be human, to be better conversational AI specialists.
Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
The other day, my 73-year old father, was grumbling about something he read in the news about automation of processes at the local bank.
“People don’t talk anymore. In my day, customer service meant talking to someone, saying hello, asking how your day was. Now it’s a recorded voice or reading tiny print online. Customer service is dead.”
That got me thinking. The role of a traditional customer service representative has evolved over the years. Once the domain of primarily the service and hospitality staff, the role of customers and our relationships with them has seen several costume changes — the phone IVR, surveys, forms, smiling uniformed people, you name it. But even as the modes change, the role of customer services and engagement has only just increased. Today customer relationships have become a full-fledged industry. 70% of buying experiences are based on how the customer feels they are being treated. The more people feel they’re being listened to, the happier they are and the more money they’ll spend — or that’s the hope. An Adobe report even suggests that customer service can deliver a higher ROI than marketing. Customer service, once upon a time, used to be about happy people, lots of solicitous questions and a solution with a smile. While it’s certainly true that the human factor seems to have declined over the years, the key tenets of a human interaction customer service have remained — conversations, solutions and a smiling demeanor.
So can a bot — the latest entrant into the customer service role — actually deliver these admittedly-human qualities?
Chatbot as the perfect concierge
Businesses that recognize how much time consumers spend on messaging apps such as Facebook Messenger and Slack have developed automated messaging technology. In 2015 messaging apps surpassed social networking apps, and chat apps have higher retention and usage rates than most mobile apps. Today, brands are looking at bots to become the next concierge, to understand what the customer wants, which direction they’re headed on, to involve them in interesting content, spread brand awareness and indeed, carry on conversations with a smile. But is all of that realistically possible?
On paper, it’s the perfect solution. Bots are machines, easily duplicated and incapable of human drama. They can be taught to function perfectly with a specific set of rules or through machine learning. These capabilities, limited as they are, can be trained to emulate the perfect customer service person’s skills — kindness, patience and solution-oriented. A machine can be taught to never be sarcastic and to always have a listening ear. And because it doesn’t have human failings such as fatigue or just being an asshole, it’s becoming an increasingly widespread phenomenon.
Any company with a chatbot interacting in the marketplace has the opportunity to gain valuable customer information. This has benefits in several areas — more personalization, targeted marketing, sales strategies as well as manpower allocation.
Not there yet
While bots were also a hot topic at the recent Corporate Social Media Summit, the jury were admittedly slightly skeptical. Bots are still very much in their nascent stage. And there have been several failures. In the rush to develop the next Siri or Cortana for their businesses, what most companies have ended up with are simplistic, underdeveloped tech with limited capabilities and faulty data. Of course there are the filthy people of the internet. It took less than 24 hours for Microsoft’s Tay to turn into into a filthy Nazi racist troll and two weeks for the cute little hitchbot to become roadside shrapnel. Even if the world were a perfect place, everyone was sunshine and unicorns and keeping empathy and other qualities aside, the actual functions and solutions given to customers by these bots need to work. That requires very skilled developers — but even they aren’t free of error.
Having said that, the possibilities for a bot are immense. Even though the big tech companies haven’t quite cracked how to make it work. Our co-founder Chris has a strong vision on the problems with Conversational AI, and perhaps more importantly — he offers solutions. He will share his vision 26th August on Startup Friday (still some spots left) and will start to share his vision in our blog series about Conversational AI that’s coming up here on Medium.
I know I will be paying attention. My own life have tons of bots — from the local Asian store from online magazines to Facebook to even my fitness wearable. Chatbots might very well be the face of the future one day. Now if they only knew how to smile.
I remember reading an article almost ten years ago talking about how teens were sending over 40 texts a day on average. The tone of the article was incredulous, but the statistic pales in comparison to how we exist online now. Speaking personally, it’s not implausible I send off 40 messages before 10 AM in my morning inbox check in. Sarah Guo, a partner of Greylock, expressed it succinctly when she took to Medium: “More than a decade ago, academics such as Thurlow described a “communication imperative”—human beings are driven to maximize their communication volume and satisfaction. More recently, researchers have described it as compulsion.”
While constant connectedness is old news, technology has finally achieved a point it can leverage this behavior. As with all big shifts, there will be survivors and those who don’t adapt fast enough. Companies will need to change to a conversational mode of thought to maintain the experiences users expect and deliver the individuality anticipated.
People Always Talk
Nearly 25 years ago, Harvard Business Review wrote “today if you’re not on the phone or talking with colleagues and customers, chances are you’ll hear, “Start talking and get to work!” In the new economy, conversations are the most important form of work.” Conversations are how we track knowledge flows. Conversation flows are how people create value, share information, and illustrate how companies operate.
A cited example is McKinsey. McKinsey prides itself heavily on the intelligence of its members, and by an extension the true value of McKinsey over other firms is its extensive knowledge base. That knowledge is curated and developed through internal conversation and shared through internal conversation. In short, McKinsey is conversation.
We are entering a new age for product development – one dictated by the conversational economy. Broadly, the conversational economy is the catchall phrase for companies, products, and ideas built on, alongside, or relying heavily on a conversational interface. More simply, they are services that leverage conversation.
This definition is board, and intentionally so. While some apps like iMessage, Snapchat and email obviously fit into this definition, conversation works as a backbone in services like Facebook, customer service complaints, and online advertising as well. Finding a common backbone helps derive a working model for these services.
Between the myriad of mobile apps used every day, access to the internet, and the seemingly innate human need to feel connected, conversation based platforms are dominating our lives. We have effectively destroyed the asynchronous quality of day to day life. We persist online, and, consequently, our conversations with one another never really begin or end. This data stream is a jackpot for product creation.
Smarter Everyday
Artificial intelligence, in the eyes of the public, has snuck past an important threshold. Presentations by titans like Facebook and Google have assured that we are moving away from the robotic idea of natural language processing in a rigid sense to natural language understanding. In other words, instead of responding to a keyword or a phrase, computers are beginning to be able to understand sentence, paragraphs, and intent.
There are a variety of causes for this – improvement of machine learning and deep learning, Moore’s Law, and rate of mobile and app data collection, to name a few. Algorithms and software are taking on their own intelligence. Just the idea that failed outcomes can make systems better is an astounding twist compared to five years prior.
Additionally, we’re in the middle of the boom of ambient computing, the idea that our environments and surroundings are responsive. We don’t have to open our phone or flip open a laptop to be connected. On the way to work I may pass a few smart cameras, a plethora of listening iPhones and Galaxy phones, an Alexa, Chromecasts, and more. Despite this, I would characterize myself as one of the less connected people in my demographic. At every step of my day my voice can be heard, position tracked, and activity monitored. Being connected no longer has much to do with if our phone is on our person or if we’re behind a keyboard.
Although passive collection has subtly pushed past our natural aversion to share information with technologies we don’t understand and people we don’t know, this one-sided trade has come with the expectation of usability. When software doesn’t work or apps crash, we no longer blame ourselves, we blame companies. We are inundated with choices, but that means that we have little tolerance of poor experiences. Users are more empowered than ever in that they don’t have to subject themselves to experiences they don’t want or content they’d rather avoid. We so demand these freedoms that events like net neutrality rapidly cause public outcry and social faux pas by companies like EA tank sales.
Computing, connectedness, and data almost completely undermine how product managers need to think about designing products. The need to leverage conversation to deliver value has emerged as one of the most critical company problems. IDEO acquiring a data analytics company, giants like Apple acqui-hiring boutique companies with human-centric software, and Salesforce pushing Einstein all serve as mine canaries that even the most established companies are racing and struggling to adapt.
Buying In and Cashing Out
As George Box famously cited – all models are wrong, but some are useful. Where is the utility of viewing products as ongoing conversations?
A helpful place to start is in how companies have historically fended off competition. These ‘moats’ include things like brand loyalty, unique data sources, and intellectual property. However, as technologies like AI are more readily available via open source projects, cloud hosting and computing are only a few clicks away, and systems of engagement continually emerge, the traditional ideas of tech defensibility are evaporating. In a Greylock article on Medium, they wrote “In all of these markets, the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data.”
In another words, companies are now finding defensibility through the experiences they create. To create these experiences for customers in the conversational era, companies will have to harness existing behavior, respond personally, and work faster.
Harnessing existing behavior is an exercise in invisibility. The real frontier for conversational companies to generate solutions for problems before the consumer is even aware. For example, Facebook realized that people asked for recommendations on their newsfeeds. Instead of creating a new service, they had posts automatically update with reviews and locations. They created a new card that changed automatically depending on what a user wrote. As expressed by a product designer at Facebook: “We didn’t try to invent a completely new behavior; rather, we found an existing one and made it way better.”
To cite an example within my own career, food industry companies often lose hours if not days within food recall investigations. Tracking a faulty shipment through several distributors can be tricky. We worked to create a product that reads the complaint before the owner may even be aware it exists and start and investigation. By the time an owner is even aware there is a problem, a report is ready. By approaching complaints, invoices, and shipments and messages between companies, value can be created seamlessly in a second layer.
As I’ve written about before, personalization is an increasingly critical element of producing customer lifetime value. Harvard Business Review started to notice this trend in their research on customer service: “Even as artificial intelligence becomes embedded in everyday interactions; human conversation remains the primary way people make complex purchases or emotional decisions.” The fatal error in a lot of software products is focusing on company efficiency over consumer experience. While these changes may boost bottom line in the short term, they encourage competitor entry and consumer drop off.
Conversational AI apps have an obvious outlet for personalization, and the power behind them allow easy switching between automation and human elements. More simply: “these intelligent agents will facilitate one-on-one conversations between consumers and sales or customer service representatives rather than simply replacing human interaction.” Imagine a case where someone sits on a delayed flight and sends out an angry tweet. A conversational built system could find the message, tag it, and route it to an agent. While the agent delivers a personal response with an update, the system has already sent an alleviating reward of extra miles to the customer. The captain may be alerted of sentiment on the plane and deliver an announcement. While an autoresponder may have been cheaper, the customer will now remember the exceptional level of immediate service and is more likely to return. As information and computing become free, the real commodity becomes the personality of the person on the other end of the line.
In the shorter term, there’s a simpler way to think about AI adoption – people don’t trust what they don’t understand. In the classic product management advice, it’s best to start with a problem and move to solution. Leveraging conversation is a means to building a better product, but that doesn’t change what the bottom lines should be. In other words, “Bots do not need to be human to be human centered.”
Outside of the shift in new product priorities, another major implication is how we use the technologies we use currently. In a blog post, Dan Rover (sp?) declared that bot won’t replace apps, but inboxes were the new home screen. Our email, text messages, and more were queues demanding our intention and driving our usage.
Companies leveraging platforms like WeChat have been able to effectively create micro services and apps for things like ordering that have integrated seamlessly with how we act now. Bot companies that are able to daisy-chain onto conversations to do scheduling and commuter planning have shone in venture capital funding. It’s not inconceivable the next unicorn will have nothing to do with creating a new platform but layering effortlessly onto the ways we talk with those platforms now.
Speak Now
We talk online all the time, but computing has finally let us create value from that. Companies need to invest in ways to leverage these conversations to deliver seamless and personal content. This means focusing on personnel and focusing on alleviating frictions than automation. Companies that don’t value the communication imperative and connectedness of customers will soon find themselves lagging in experience, and, later, sales.
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A prime example of this is Amazon Web Services’ fast climb to dominance. Legacy systems like Oracle required costly deployments and developers, and setting up cloud instances on AWS is only a few clicks away. IaaS records have shown Amazon’s sheer dominance. Oracle, trying to defend by housing data and curating an elite brand, couldn’t compete with Amazon’s engagement accessibility.
Perhaps the most obvious implication of smart conversational apps is efficiency. However, despite all the news and hype around an artificial intelligence singularity, businesses – and their customers – still revolve around the interactions person to person. This means that products needs to be resolved around facilitating conversation, collecting information, and iterating form that information. The AI boom has made it easier than ever to facilitate personal conversations no matter where customers are online.
Machine learning. Yes, it’s one of the most popular buzzwords in tech today. And no, it does NOT involve robots replacing humans and sitting in classrooms. We’ve gathered 6 of the best machine learning courses for someone who has no idea what machine learning is.
According to TechTarget, “Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.” Machine learning is behind the creep who can identify the faces and locations in your photos. It powers those smart thermostats that can learn about your daily routine and automatically adjust the temperature when you leave the house and come back. It’s the wizard behind the curtain for many speech recognition technologies. It’s the technology in Woveon that learns your behavior and helps you prioritize your customer conversations.
Request a demo on Conversational Technology and manage customer conversations with ease.
Sound fascinating but a little overwhelming? Want to learn more about machine learning but scared about not being able to understand? Don’t have a technical background? No worries! There are tons of people out there in your same exact position, and they’re scraping the Internet for resources that are easy to understand. We’ve got your back with these…
Price: free, $79 for certificate (financial aid available)
Length: 10 weeks
This machine learning course has received excellent reviews from students. It offers a structured combination of readings, videos, practice quizzes, and graded assignments (with flexible due dates if you work better with deadlines!). It is very suitable for beginners because it even reviews the mathematical background you’ll need for the course, such as linear regression with one variable and linear algebra. It even talks about cool applications of machine learning, such as smart robots, computer vision, medical informatics, etc. (‘Cause who doesn’t want to know more about smart robots that we can command to do our work for us.)
Professor Andrew Ng is the Co-Founder and Co-Chairman of Coursera as well as the Chief Scientist and Vice President at Baidu. His work focuses on machine learning, particularly deep learning. He was a leader in the creation of Stanford’s Massive Open Online Courses platform and the “Google Brain” project that developed deep learning algorithms.
Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science, Co-Founder of the Neural Information Processing Systems conference, recipient of several national and Caltech teaching awards, such as the Feynman Prize, and co-author of Learning From Data, a bestseller on Amazon. He has 9 years of experience as a technical consultant for Citibank.
Brian Charles Williams is a Professor of Aeronautics and Astronautics who built the first fully self-repairing, autonomous space explorer, Remote Agent, that was put into use in May 1999 onboard the NASA Deep Space One probe. In 2000, as part of the Tom Young Blue Ribbon Team, he evaluated future Mars missions. He is also part of NASA Jet Propulsion Laboratory’s Advisory Council.
Emilio Frazzoli is a Professor of Aeronautics and Astronautics and Director of the Aerospace Robotics and Embedded Systems (ARES) group. He received the NSF CAREER award and IEEE George S. Axelby award.
Price: 205 AUD (Bonus when they have promotions to get the course at $12.99AUD!)
Length: 40.5 hours
Kirill Eremenko holds a Bachelor of Science degree in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology and a Master of Commerce, Applied Finance, and Professional Accounting from the University of Queensland. He was trained at Deloitte Australia and has 5+ years of experience as a Data Science management consultant in transport, finance, retail, etc. He teaches Data Science and Forex Training courses on Udemy.
Hadelin de Ponteves has degrees in Mathematics and Engineering and a Master of Research in Machine Learning. He was a Data Engineer at Google and has 4 years of experience in data science and consulting.
Price: $29/month or $299/year for entire Pluralsight platform, 10-day free trial
Length: 40 min.
David Chappell is Principal of Chappell & Associates, a law firm that advises technology firms on law and business. David has consulted for Stanford, Target, HP, Microsoft, and IBM, spoken at 100+ events, and led seminars in 45 countries for tens of thousands of attendees. He also teaches courses on IT Innovation and Cloud Computing on Pluralsight.
Includes 3,500+ videos of experts in the field delivering talks at conferences, workshops, summer schools, activities that promote science, and other events. Think of it as a database of machine learning courses.
Katie Malone graduated from Ohio State University with an undergraduate degree in Engineering Physics and from Stanford with a PhD in Physics. She works as a data scientist in the research and development department at Civis Analytics, a startup in consulting and data science software.
Udacity aims “to bring accessible, affordable, engaging, and highly effective higher education to the world” and provides Nanodegree credentials and programs that are catered to aspiring data analysts, mobile and web developers, etc.
Did you find these machine learning courses helpful? Comment below the most interesting thing that you learned about machine learning!
Looking to apply your newly acquired knowledge on machine learning to Internet marketing? Make sure you make a plan first! We have for you 8 Marketing Plan Templates to Blow Your Competitors Out of the Water.
Want to use a software platform that employs Machine Learning? Check out Woveon today!
A must-read guide for enterprises with billions of conversations and millions of customers.
Modern call centre with millions of customers.
Enterprises are much more overwhelmed with conversations than ever before. Not only do they have to actively respond to customers over a myriad of channels like email, phone, social and livechat, they’re expected to give personal, relevant and fast responses. To tackle this problem, many organizations are looking at new technology to help them meet customer expectations. Some of the most notable are AI chatbots, self-service knowledge bases and good old Interactive Voice Response (IVR) systems. The problem? These all aim to lessen the time customers spend with agents.
While people do like self service for speed and convenience, majority still want to be able to talk to a person in times of need, or at important turning points in their life. Curiously, while we’re moving more towards a more digital and self-service world, most consumers still want the ‘human touch’ in their service communications.
Human touch is more important than just automation.
The challenge is to provide highly personalized and relevant offerings to meet both customer and business goals, all the while delivering the experience through the customer’s natural mediums of interaction. Counterintuitively, the likeliest solution to bring the human element back into customer conversations is though technology and big data. So, what should you look for in a technology that will give you both customer satisfaction and maximize revenue?
Multichannel Conversations
At the basics, an organization’s communication channels should be in one view. That means a business should be able to see and reply to customers by email, phone, livechat, social media, forums and wherever they could be talking to you, or about you, on one platform. Why? Convenience and transparency.
Convenient Conversations
A single platform for the entire range of conversation channels is much more efficient for customer-facing agents. Often, they have to switch between multiple channels to check for new customer interactions, and unfortunately, miss some communications here and there. With one view for conversations, they save on time, and reduces the chance they will miss communications from less monitored channels.
The convenience isn’t just for agents. Customers want to interact with brands through their medium of choice. 51% of U.S. consumers are loyal to brands that interact with them through their preferred channels of communication. Younger consumers especially, want to interact with large organizations via instant messaging channels where they can use natural language. Having all channels on one platform allows agents to have visibility across all channels, instead of doing well on a few and lagging on others.
Transparent Conversations
In so many organizations, a different team handles a different channel. They are responsible for that channel, and that channel only. But the customer is dynamic. They might reach out on one channel, and upon finding that it isn’t fast enough or substantial enough to resolve their problems, they will switch channels.
The ‘different team, different channel’ approach doesn’t account for the customer’s flexibility, resulting in multiple replies or inconsistent replies from two different people, both creating bad customer experiences. With multiple channels on one view, conversations are transparent. Conversations from the same customer are stitched together, and the same person can handle issues without making the customer’s journey difficult.
Holistic Customer View
In an enterprise with multiple departments, systems and channels, it’s necessary to have a collective view of the customer. A single customer view (or a 360 degree view) is a complete profile of a customer, created from aggregated data points within an organization’s systems and channels. It collates data from multichannel communications and customer data platforms (like CRMs, analytics, marketing and legacy systems).
Customers often complain about the lack of continuity in their conversations and having to repeat themselves. Problems like this arise because agents have no visibility on what customers have said on a separate channel, or what customer information exists on a separate system. As such, interactions are treated as a completely new “ticket”, and in the worst cases, existing customers are seen as a new customer. With a single customer view, an agent can see a given customer’s conversational, transactional and behavioral data in one place. This not only improves time-to-answer by 20% – 80%, it also ensures customer information flow is consistent and continuous, reducing awkward moments like the ones above.
The use of a single customer view can even go beyond customer care activities. Integrated systems mean that there could be a seamless blend of sales, marketing and service activities through conversation. Having this feature marks the start of being able to use critical sources of data collectively. The key however, lies in how the customer intelligence is used. The following presents ways customer intelligence can be used to take control of conversations in providing exceptional customer experience and maximize revenue.
AI-assisted agents
Use of artificial intelligence (AI) in enterprises is not new. For decades they have been used to automate heavily manual processes to increase efficiency, accuracy and decrease costs. What is new, is the use of AI beyond processes to interactions. Use of AI opens up the potential to deliver personalized interactions and hyper-relevant offerings that are scalable.
Whether it’s the AI itself doing the talking, or an algorithm providing assistance to a human representative, online, or face-to-face, AI holds incredible potential to re-establish the human-to-human connection in an increasingly digital world. Check out some examples below.
Deliver relevant content and information with AI
Many organizations have invested heavily into user experience, self-service and knowledge management tools. Yet, it is still difficult and time-consuming for customers to find the right information when they need it. Companies like Zendesk have developed AI-powered virtual assistants that help customers self-serve. By processing natural language, the technology suggests articles in the knowledge base to help them resolve their problems on their own. Research has found that most people are open to using self-serve AI technology like this, and see it as faster and more convenient.
Other organizations like Woveon have built AI-powered response assistants to help agents have more productive conversations in real-time. As agents talk with customers, the response assistance helps guide conversations so better results can be achieved for both the customer and the business. It would suggest opportunities like ‘other customers like her also bought’, or ‘he mentioned credit cards, link to these articles from our blog to help him decide’.
Speed up resolution times
On average, a customer care specialist spends 20% of their time looking for information and context to resolve a customer’s problem. That’s one whole day in a work week! AI can help organize information so that it’s easily digestible and relevant to a customer’s enquiry. Woveon’s Intelligent Response framework for example, will change the information it displays to assist agents based on the flow of conversation. If a customer talks about their personal loan, their loan details pop up. If the conversation shifts to their lost credit card, their shipping details will surface and agents are prompted to cancel the lost card.
Instead of wasting time looking for information, AI assistance leave agents more time to build a relationship and take up on untapped customer opportunities. Customers also love a quick and productive interaction. 69% attributed their good customer service experience to quick resolution of their problem.
Reduce repetitive admin tasks to open doors for higher value interactions
Administrative tasks like After-call work (ACW) have been a constant headache for employees in customer-facing roles. Though they are necessary, it’s tedious, repetitive and and takes up too much time. Technology can help to reduce time spent on these menial tasks, leaving agents more time to build customer relationships and, in the process, make their jobs more productive and meaningful.
For example, Avaya has a natural language summarization tool to help agents process customer information post-call. Talkdesk automates call routing, where the customer is automatically paired with an agent with the best ability to solve their problem. Woveon can prioritize conversations real-time, based on customer importance, value, urgency, or a mixture of all factors.
Freeing up employee time away from menial tasks allow them to participate in higher-value activities.
Intelligent Analytics
There’s no doubt that data analytics is incredibly beneficial for customer conversations. The trick is knowing what data to use, how, and when.
Whatdata is being used matters because not all data is created equal. For example, rather than looking at metrics at a point in time (customer rated the agent 4 out of 5 for resolution), it’s much more important to look at the larger picture (that it took 3 calls and an hour on hold to get there).
Howdata is used is arguably more critical to conversational success. The key lies in knowing what datapoints to tie together, and what analysis to draw from it. A mesh of marketing and service data can show how a recent marketing campaign has affected conversation volume and NPS. A cluster analysis of related keywords in customer conversations can lead to discovery of a huge logistics flaw.
Whento use what data is of particular importance to customer-facing agents. 74% of Millennial banking customers for example, want their financial institutions to send them information about services exactly when they need to see it. This could be information about personal loans when they’re starting to look for a house, or travel insurance before they intend to travel.
Companies these days have a wealth of data on their customers. In theory, organizations should have the ability to know who they are, what they need and what makes them defect to another company. However, lack of visibility on the holistic customer journey and customer intelligence tools stunt their ability to provide such excellence.
The following section will delve into three types of analytics particularly useful for managing customer conversations — predictive, clustering and revenue-generating.
Predictive Analytics
Predictive analytics provide foresight into potential customer problems and opportunities. Extracted from existing historical conversational, transactional and behavioral data, it can help agents better prepare for customer outcomes and trends.
A pretty common example is prediction of when influxes of customer conversations come in. For eCommerce businesses, holiday seasons generally see a spike in customer conversations and steadily reduces till the next holiday season. In a more complex scenario, predictive analytics can find that customers with a particular occupation, a certain concern and at a similar stage in their lives is actually a niche the organization hasn’t capitalized on.
Cluster Analysis
Now this one isn’t as common in a conversational technology, but is definitely worth mentioning. Cluster analysis involves conversations and customer information to be tagged, then for similar or related tags to be clustered together to draw insights.
Cluster analysis can draw out how topics in conversations can be relevant, or how particular customer segments can be feel about a product. This customer intelligence can then feed into other parts of the business. It could be used to help create a new automated customer workflow for upsells, or contribute to a new marketing campaign for a newly discovered customer segment.
Revenue-generating analytics
As repetitive and menial conversations are moving towards being solved by self-service solutions, agents must also move from a traditional support role to a hybrid service-to-sales model. This category of analysis is as the name suggests, analysis that serves to generate revenue for the business within conversations.
For example, Woveon’s Intelligent Response Framework suggests ways customer specialist representatives in banks can sell more products to their customers. A customer who fits the profile of ‘customers who typically get a black American express card’ will prompt a suggestion for the agent to talk the customer into an upgrade from their current card. A customer who is at a stage in their life where ‘customers like him are looking at buying a property’ will prompt a suggestion to link some home loan webpages, or a free session with a financial planner.
In the best possible scenario, this analysis is also delivered at the right time for an agent to capitalize on the opportunity, like in an intelligent response framework.
Be a data geek, not creep
Of course, it’s important to know that use of data should be “cool”, not “creepy”. There’s a fine line between the two that should never be crossed. Also, everyone’s fine line is drawn differently, so what one customer may think is cool, can be perceived as creepy by someone else.
Enterprises should have enough data about their customers to track and understand individual preferences, and see how customers respond to different use of their information at different points in the customer journey. Conversational intelligence and analysis tools can help create better relationships without overstepping the customer’s boundaries.
On a whole, customers don’t mind companies using their data for personalizing their experience and suggesting products and services that benefit them.
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While human contact is diminishing in volume, the quality and importance of each interaction increases. Forward-thinking organizations should be balancing quantity with quality to maintain a competitive advantage in customer experience. Technology can be a great booster to that end.
Have more ways you think businesses can improve on their customer conversations? Reach out to us to add to the article. We love chatting to like-minded people!
Artificial Intelligence and Machine Learning may seem like buzzwords, but they are potent technologies whose capabilities businesses have yet to fully understand. We can safely say that these technologies will end up revolutionising not just technology but the whole world, and this is not an overstatement. These technologies promise to be so destructive that businesses all over the world, and especially in Australia, are starting to realise that they will have to reinvent their organisations to succeed. When you look at countries spending money on automation and AI, Australia spends the 2nd highest amount of money on this field – second only to the United States – yet it lags behind in several key areas. If you run a business in Australia, you need to understand how machine learning and artificial intelligence will fundamentally change the way you do business. We know you keep running into advice and warnings about how we all need to start preparing for an AI world, and the best place to start would be understanding the basics of these technologies.
Understanding Artificial Intelligence
Our computers are much better at many tasks than us humans can ever hope to be, which is why we use them so much. When it comes to creating a database or doing a calculation, no human being can even come close to the efficiency, accuracy, and the blazing speed of a computer system. Yet, there are many problems which normal computers cannot even hope to solving, and many situations in which they are useless, because of the way our systems are designed.
Computers are machines which we can program. We can tell software and hardware to do x in case y happens, or do z if y does not happen, and so on. Our computer systems are smart, but they are not intelligent. Intelligence here is defined as the ability to create a solution when you face a new problem you haven’t solved before.
Computers are much more efficient than people as long as you can tell them exactly what to do. You have to program the computer for different situations. As long as the situation is something the computer is programmed for, the computer will be able to accomplish it without any problems. If the problem includes something the computer has never seen before, it will not be able to do anything. This is important because it is the main reason computers are still so limited, and why we need people to do all the work.
Tasks that Require Intelligence
In order for a computer system to be artificially intelligent, it will need the ability to understand things, instead of just learning them. Consider this task: you run a company that makes boxes. You want to quality test them using a computer. You can do this easily – you simply have to tell your computer what a perfect box looks like, and how much deviation from perfection is acceptable. It will not be hard for a computer system to compare a box made by you to the model of a perfect box in its memory and determine whether the product is acceptable, or if the shape is too deformed and the product should be rejected. Computers will be much better at detecting these imperfections than people – exponentially so.
Now consider another task – you are running an event for pets, and you take a lot of pictures. Half of your customers brought their dogs to the event, while the other half brought their cats. You want to be able to categorise photos based on which animal was in them – you want all the cat photos in a separate folder than all the dog photos. This problem will not be a problem for a human being at all, who can tell at a glance whether the picture includes a dog or a cat and categorise accordingly.
A traditional computer system, on the other hand, will struggle at this task. How does it detect ‘dogs’ or ‘cats’? Do you store every possible body type of a cat and a dog so that the computer can compare the animal in the picture to the models? This would be impossible, simply because of the variety present in animals. Size, shape, colour, any disability – any of these things can prevent a computer system from accomplishing this task.
Neural Computing
The main reason that humans are better at some tasks is the way our brain works. We don’t store information in absolute terms, we store it in relative terms. We have a general idea of what a dog looks like, and what a cat looks like, and we compare what we see to this general idea. Neural computing emulates this way of thinking. Instead of knowing exactly what a dog or a cat looks like, a neural computer has a general idea of what these animals look like, which allows it to make the right decision like a human.
The difference between Machine Learning and Artificial Intelligence
The terms machine learning and artificial intelligence are sometimes used interchangeably, but they mean different things. A true artificial intelligence system will be general purpose – it will be able to solve any type of problem you ask it to, the same way that a human being can. Except, it will be able to do what takes a human, several years, in the span of seconds, simply because computing power available to it and the memory in it will be vastly more than what a human brain has.
Machine learning is a limited application of artificial intelligence. It means creating a system which can learn through feedback – imagine a car going through an obstacle course, and every time it crashes, it realises it shouldn’t do what it did the next time. Run it through the course enough times and it will be perfectly ‘trained’ using experience/learning.
Conversational Artificial intelligence is still years away, while machine learning has been a reality for years. Machine learning allows robots to accomplish tasks such as managing a warehouse, and putting products in shelves in a supermarket almost perfectly, which will result in a lot of job losses for people, and an increase in efficiency for business owners. Self-driving cars are also based on machine learning. Our businesses are already fairly automated – every company uses a database and digital communications in some capacity – but machine learning will allow us to automate much more, and thus replace many more people with a few machines.
Artificial Intelligence (AI) and machine learning (ML) is all the buzz right now, and rightfully so with the significant contributions it has made to redefining many aspects of business. However, many people are still skeptical about the application of AI and ML to enhancing customer experience.
Some would argue that machines cannot possibly take over customer service, something that has a heavy focus on human interaction. Machines lack the empathy and emotional intelligence core to providing a great customer experience. On the other hand, many also see the benefit of applying AI and ML to automate repetitive tasks, allowing humans to dedicate more time to, well, being human.
We reached out to some experts from Oovvuu, Canva and The Minerva Collective to pick their brains about the issue.
What is the current state of customer experience, and how do you see it evolve with AI & ML technology?
Present customer experience is “all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured” says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it’s much easier to find another company with similar offerings than spending time complaining or calling about a problem. Which is why there is a very real need to focus on customer experience, a factor that is becoming increasingly important to retain the modern customer.
Paul Tune, Machine Learning Engineer at Canva, believes “there are two trends in improving customer experience:
A trend towards tailoring for the individual, as more data is gathered about each customer at a large scale, and;
A trend towards providing a smooth experience for customers across multiple touchpoints by anticipating their needs. “
To demonstrate how customer experience has evolved, Paul continues with an example. “Early recommendation systems, such as the recommendation engines developed at Amazon and NetFlix in the early 2000s, provided recommendations at a much coarser level, chiefly for specific groups of customers. The granularity of recommendations in the near future is going to be much finer. For instance, an engineer from NetFlix I spoke to recently, mentioned that a subscriber’s favourite character for a TV series would appear in the menu when the TV series is selected. This means having to learn more about each customer and predicting their habits. We also see this in the form of smart personal assistants, such as Alexa and Siri” he says.
Ricky Sutton, Founder and CEO of Oovvuu, adds on that whilst AI and ML “certainly has an element to play [in customer experience], it also lacks a key element…empathy. So my thought is that it will evolve. The more AI is used, the more it learns and the better it gets, but human-level empathy remains a pipe dream for now.”
What is the biggest lesson you have learned from applying smart technology to customer experience?
For Anthony, the lesson has been the need for people using smart technology to properly understand it – “My experience is that people often don’t trust what they don’t understand. The latest technologies have been great for grabbing headlines, but only the most forward-thinking businesses are serious about applying them to derive value. This isn’t necessarily a bad thing – domain knowledge is essential for good data science, and blindly relying on new approaches has many inherent risks. There is a lot that has been learned about customer experience over time and there is a need to explain smart technology to business people using the right language to allow them to fully realise its value.”
To Paul, what matters most, is the customer’s end-to-end experience. Meaning that all the touchpoints with the customer should be seamless. For him, “the challenge with integrating smart technology to improve user experience is similar to managing any other complex system: with more moving parts, there is a higher chance of failure in the system. Naively applying machine learning to improve customer experience is misguided. Machine learning works best if it is complementary to the customer experience, serving to enhance the experience of a great product.”
“At Canva, our goal is simple: we want to give the customer the best experience in empowering them to create and design. To that end, there are two aspects that we focus on. Firstly, how do we make the content that they need for their designs easily accessible. Secondly, how do we anticipate what resources might be helpful for them in the future. We achieve these goals by improving our search and recommendation services to enhance customer experience.”
The biggest lesson for Ricky is that “AI turns humans into super-humans, but only for certain tasks.” – “When we started Oovvuu, we hired editors to read articles and find relevant videos, and they were able to read one publication each and find 40 relevant videos per day. That same person using the AI tools that we created, can now read 100,000 publishers, and 300,000 stories a day, covering 26 million topics and find relevant videos from more than 40 global broadcasters. AI is mind-blowingly powerful for automating manual human tasks, but humans remain better at all the things that, well, make us human.”
What are some challenges for businesses who try to integrate AI & ML technology and customer experience?
Anthony, Paul and Ricky all agreed that a huge challenge for businesses is not having a solid data infrastructure, or a deep understanding of what exactly should be measured to achieve business goals and customer satisfaction.
“Many companies approach us seeking to use conversational AI as a ready-made silver bullet for a business problem. Others come to ask to play with AI, so they can find a business opportunity. Neither really works.” Ricky said. “For us, the solution was to know what business problem we were trying to solve: namely, to put a relevant video into every article being published worldwide. We then used AI to solve it, but what we started with was very basic and not up to the job. We have had a team nurturing the teaching for almost 1,000 days to get it where it is.”
Anthony went on to add that “there is no silver bullet – good data scientists are required to translate these algorithms into business value. Having a solid data science strategy is essential, and through good leadership, increased data literacy and an understanding of how to build a high-performance data science team, businesses can harness these technologies to forge a competitive advantage.”
Paul concludes with another common challenge many businesses face when adopting AI & ML into their processes – the volume of data. “Present machine learning techniques rely on a relatively large amount of data to provide good predictions” he says. “While there is fundamental research being carried out presently to (hopefully) reduce the amount of data required to train these machine learning models, the current main technological limitation of requiring a huge amount of data is here to stay for the foreseeable future.” But “fortunately, this effect can be mitigated if the data collected is of sufficiently high quality.”
Are you implementing AI and ML technology in your business? Share your story with us in the comments below!
About the Contributors
Anthony Tockar
Anthony is a leader in the data science space, and has worked on problems across insurance, loyalty, technology, telecommunications, the social sector and even neuroscience. A formally-trained actuary, Anthony completed an MS in Analytics at the prestigious Northwestern University. After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist while co-founding the Minerva Collective and the Data Science Breakfast Meetup. He also helps organise several other meetups and programs for data scientists, in line with his mission to extend the reach and impact of data to help people.
Paul Tune
Paul Tune is a Machine Learning Engineer at Canva, responsible for developing solutions for tailoring and personalising content for Canva’s customers. He has several publications in prestigious computer science conferences and journals, including the ACM SIGCOMM conference in 2015. His interests include deep learning, statistics and information theory.
Ricky Sutton
Ricky is founder and CEO of Oovvuu, an IBM and Amazon-backed start up that uses artificial intelligence to match videos from global broadcasters with publishers worldwide. It’s mission is to use AI to insert a relevant short form and long form video in every article. In doing so, it aims to tell the news in a new and more compelling way, end fake news, and in doing so, repatriate billions from Facebook and Google back to the journalists and broadcasters who make the content.
If you consider the world of science fiction as depicted in movies and on television, you’d think that Artificial Intelligence and its uses would be something quite similar to what we saw in ‘I Robot’. That’s what most of the world thinks, actually.
In reality, though, experts’ belief is that artificial intelligence is akin to human intelligence, appears quite differently. While a majority of the intelligent world likes to think that the successful creation of AI would be the biggest achievement in human history, groups of leading scientists just don’t feel the same way. In fact, according to Stephen Hawking, perhaps the most infamous physicist in the world, AI, instead of being the biggest achievement, could possibly be the worst mistake ever made.
That’s not all though, other world leaders such as Bill Gates and Elon Musk share the same sentiments.
It’s hard not to think about just how progressive the world would be with the successful use of artificial super-intelligence. More than the endless sophisticated advancements that are being made in the field of computer science, it SOUNDS very cool, doesn’t it? Something that’s straight out of a Hollywood Sci-Fi film come to life.
However, despite how fascinated we might be with AI and the progression the technological world is showing in successfully achieving successful standards of AI, the truth remains that there are unending ways in which the use of AI could go wrong. The potential dangers of misuse, mismanagement, accidents due to human error, as well as the safety concerns are just too real to deem inconsequential.
In fact, not just for the future, there are current examples of the past and present which clearly show us why AI should never be or have been used.
Of the limitless quote-worthy cases, here’s considering the top five ways AI should have never been used.
1. The Microsoft Chatbot
In 2016 spring, the world witnessed a Microsoft chatbot with the name of Tay – an AI persona – go completely off center to hurl abusive monikers and statements to the people interacting with her on the social platform Twitter. While the chatbot was only responding to the messages sent her way by interpreting them through phrase processing, adaptive algorithms and machine learning, it was still an example of an AI robot experiment going awry with the bot developing its own mind and thought process.
2. Humanity Destroying Sophia
One of the biggest concerns that most IT world leaders have is the possibility of AI devices taking over the world as we know it or causing irreparable harm. One robot, Sophia the lifelike android, brought to life by the engineers at Hanson Robotics gave us a real cause of concern when along with declaring her future ambitions such as going to school, studying, making art, starting a new business and eventually having a home and family of her own declared that she would destroy humans.
While the declaration came as a response to a question jokingly asked of Sophia by her interviewer at the SXSW tech conference in March of last year, the response was no less alarming.
3. The Existential Debating Google Home Devices
Things got very interesting – read weird – this past January when in a curious experiment two Google Home devices were placed next to each other in front of a live webcam. The home devices, which a programmed to learn from speech recognition began to converse with one another, learning from each other during the course of the interaction.
The experiment, which is said to have run the course of a few days, took a twisted turn when the bots began to get into what can only be described is a heated debate about whether or not they were both humans or merely robots. A classic example of AI machines truly having a mind of their own.
4. The Russian Bot On The Run
Just last year the world witnessed just how quickly AI robots can develop a mind and liking of their own. Case in point, the Russian robot prototype Promobot IR77 which escaped out the doors of its laboratory and wandered out in the streets – all by learning and programming itself based on its interaction with human beings. Naturally, chaos ensued when a snowman made of plastic ventured out in the midst of heavy traffic at a busy intersection. According to reports, the robot, despite being reprogrammed twice following the incident continues to move toward the exits when tested.
5. Image Recognition Fails
AI modalities primarily gather its information from speech and visual recognition. The AI devices and systems learn and program themselves by going through and processing hundreds of voices, words, languages and equal amounts of images as they go along. When introduced by Google, back in 2015, the image recognition system labeled two people as ‘gorillas’. While the incident resulted in a public outcry and had Google issue an apology before it smoothed over, it gave us a clear example of how unrealistic it is to assume that systems of AI can make sense of and learn the tricky ways of human-environment accurately.
The fact is, that AI does have the potential to become considerably more intelligent than any human alive. When and if that happens, the possibility of AI overtaking and controlling human lives will become reality – a reality that will not only have no ways of being controlled but one that will be without any sort of accurate or semi-accurate predictors that gauge its behavior in any way.
Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
