Q&A With Datafloq Founder, Mark Van Rijmenam
Dr. Mark van Rijmenam is an international keynote speaker on the future of work and the organization of tomorrow. He is the founder of Datafloq.com, a leading content platform on emerging technologies and he is also the author of best-selling books on big data, blockchain, and AI, including The Organisation of Tomorrow. We talked with […]
By Dr. Mark van Rijmenam
December 28, 2020
Dr. Mark van Rijmenam is an international keynote speaker on the future of work and the organization of tomorrow. He is the founder of Datafloq.com, a leading content platform on emerging technologies and he is also the author of best-selling books on big data, blockchain, and AI, including The Organisation of Tomorrow.
We talked with Mark about his vast knowledge and expertise in AI and new technologies for the future. Let’s get into what he had to say about many topics such as COVID-19 AI assistance, conversational AI, and current obstacles for developers and AI experts today.
What is your background and/or expertise in this space?
I have a diverse background but in recent years, specialized in how technology changes organizations and society. I am a digital speaker and the author of three best-selling management books on big data, blockchain, and AI. My latest book – The Organisation of Tomorrow – details how AI, blockchain, and analytics turn your business into a data organization. I am the founder of Datafloq.com, a leading content platform on emerging technologies and recently I founded Mavin.org, a tokenized ecosystem of tools to fight misinformation, bad bots, and online trolls through crowdsourced trust. I hold a Ph.D. in Management from the University of Technology in Sydney, where I did research on how big data, blockchain, and AI are changing organizations. I am a board member of the 2Tokens Foundation in The Netherlands, where we develop a roadmap towards realizing value from Tokenisation.
How do you work with chatbots and artificial intelligence in your current role/industry?
Within Mavin we are building AI to automatically detect which articles online you can trust. We use various tools and are building our own machine learning model to be able to label articles on trustworthiness. At the moment, we do not have any chatbot within Mavin, or Datafloq, but I would like to implement one in the future.
How has chatbot technology and artificial intelligence adapted, and in some ways accelerated, amid the Covid-19 pandemic?
I think that AI, and with that conversational AI, has accelerated since the start of the pandemic, among others due to the rise of the digital employee. The current crisis has shown that now, more than ever before, this has become crucial for organizations. After all, organizations with a digital mindset and a data-driven culture will be able to easily switch to a remote-working organization (if they hadn’t done so already). Artificial intelligence is crucial for a digital workforce. The employee of tomorrow will be a digital employee, and the organization of tomorrow will be a data organization. Depending on how long this crisis will last, it is likely that the world will operate differently once we come out of it. Digital, remote, employees, virtualization of events, activities and meetings, automation of processes, and customer touchpoints using AI, emptier offices, happier employees, and more chatbots.
What are some best use cases of conversational AI and why?
The best use case for conversational AI is the customer service department. Groundbreaking conversational AIs such as Google’s Meena and Facebook’s BlenderBot, both released in 2020, have demonstrated that the “brute force” approach is effective when applied specifically to chatbots. Meena and BlenderBot have 2.6 billion and 9.4 billion parameters, respectively, which are only tiny fractions of GPT-3’s 175 billion. It may only be a matter of time before these models pass the Turing test by expanding to the scale of GPT-3, making them virtually indistinguishable from humans in short text conversations, which is especially useful within the area of customer service. In addition, it is an area where an organization can achieve clear benefits from employing a chatbot such as fewer call center agents.
What are some unexpected uses of artificial intelligence users might encounter in their everyday lives?
An unexpected use case of AI is that in hiring your next employee. AI is the perfect candidate to improve your hiring process. You can use the technology to quickly find the needle in the haystack by analyzing millions of social profiles, thousands of resumes, and quickly detect a list of potential candidates. AI can then automatically interact with these candidates in an engaging manner to build a healthy pipeline of the best candidates. The company Arya is one of the companies that offers organizations intelligence-driven talent acquisition. Especially when facial recognition comes into play to screen many more candidates in less time, resulting in finding better candidates for the job.
What do you think is the biggest obstacle bot developers face when trying to create a conversational bot? What are some ways to overcome these obstacles?
The biggest hurdle is the enormous amounts of data required to train an advanced bot. As GPT-3, BlenderBot, and Meena have shown, the sheer number of parameters required to train a bot can be a huge barrier to entry. Chatbot developers could overcome this hurdle by focusing on a very narrow domain, requiring a smaller dataset.
What is the biggest pain point for chatbot developers or chatbot users today?
Avoiding bias in your chatbot is a large pain point for developers. AI is almost always trained using biased data and developed by biased developers. Ensuring an unbiased chatbot is a massive challenge for developers.
What technology or functionality is missing from chatbots that you think will help with consumer engagement or adoption?
Technology that enables you to perform a normal conversation. Too often, websites use a chatbot on rails approach resulting in a very limited chatbot that more often than not causes irritation with the customer. New technology such as GPT3 helps, though an exclusive license with Microsoft might harm this.
A recent poll to our Facebook audience revealed that they find the biggest drawback of conversational AI to be users’ fear of its capabilities in the future, such as automation overtaking jobs. What solutions to this concern would you propose?
There is no technological solution against this. AI will take over many of our jobs sooner or later. Already we see that AI can perform many functionalities much better than we humans can. Only recently, researchers from Salesforce showed that the AI economist is better capable of running a society, making it more equal, than we humans. The focus should not be on preventing AI from taking over jobs but organizing our society in such a way that humans and machines can exist in harmony.
Our audience also prefers to develop and interact with more voice bots in the future. Why do you think this preference exists, and what advice do you have for new developers?
The ability to talk to a computer as if you are talking to a human has been a vision of the future for very long. That vision is still quite far away I believe, but we are getting better at it. Being able to talk to a computer is a lot easier, and faster, than having to write to a computer, which explains this preference.
What is the “next big thing” in making artificial intelligence more human-like?
I would say the capability to hold fluent conversations, whether in writing or using speech. Technologies such as GPT-3 certainly contribute to this and the recent article in The Guardian written by GPT-3 shows how far we have come already. In the next 5-10 years, these developments will only go further, resulting in ever-more human-like AI.
What conversational AI trend(s) do you expect will take over the industry in the next year? The next five years?
The main trend that I see is more advanced chatbots being implemented for customer support. Thanks to new technologies, these chatbots will become more advanced and will be able to connect to multiple data sources resulting in more relevant answers to customers’ questions.
There is so much potential in automated conversation, automated two-way texting,
Real automated conversations between a human and a robot on your phone through text message
Is it important to have a wide array of tools and platforms available for conversational AI development at this stage of the market? Or do you think focusing all our efforts on just one or two platforms is better? Would interoperability across development tools, libraries, and platforms help the market grow?
Similar to other areas in technology, I believe that the best way forward is enabling interoperability across tools, libraries, and platforms. This would allow chatbot and application developers to create a tailor-made solution for the challenge they aim to solve, without being restrained by one or another platform. However, it is important that the various tools can collaborate with each other, hence the need for interoperability, which could be a catalyst for the industry.