[July 25, 2020] by Kathy Scott, PhD, and Bridget Sarikas
SERIES INTRO: Inspired by a children’s book written by best-selling author Peter H. Reynolds titled The Word Collector (2018), we decided to collect some of our favorite words like the book’s main character, Jerome. Jerome is a little boy who is enthralled with the magic of words all around him. He eagerly collects words he hears, sees, and reads – powerful words, simple words, sad words, and dreamy words.
This is the fourth in our Word Collection Series where we thoughtfully select and write about words, apply them to the world of leaders, and then empty them into the wind for others to collect and share. Why? Because words matter. They are a powerful force that can be used constructively, or destructively. Words have energy and power. They can help bring healing, laughter, hurt, and harm. They can bring hope!
So here are a few of our interesting words and phrases – some are new, some have new meanings, some are worth reflecting on, and some are just marvelous to say.
Oxymoron. The combining form oxy- is used in the formation of compound words and means “sharp,” “acute,” or “wise.” The definition of a moron is a very stupid person. Oxymoron is a term used for a word or phrase that combines two contradictory words, words that are opposite each other or words that are unrelated (Merriman-Webster, 2020). Some great examples of an oxymoron are “the deafening silence” and “only choice.”
Oxymoron words and phrases often cause us to pause and think twice about their meaning. So, for some comedic relief, we offer a few more for your consideration: “working vacation,” “a little pregnant,” “accurate rumors,” “fuzzy logic,” “seriously funny,” “absolutely unsure,” “advanced beginner,” “parking or drive-thru service only,” and “awfully good.” This is so much fun we could do it all day long, but we won’t!
The word “oxymoron” itself is an oxymoron – combining the concepts of “sharp” and “wise” with “stupid” or “foolish.” How delightful is that! We love that oxymoron is an oxymoron!!!
Consumer Internet of Things (IoT). Our technology is exponentially connecting us to each other and now there is a growing trend to also connect us to our “things.” (Think Jetsons!) This connection evolution is driven by the continuous exponential reduction in the technology costs of computing, storage, and connectivity. Since the 1990s, each new phase of evolution has built on the last and carries greater and greater power to influence and penetrate our lives.
This “connection evolution” is really a “connection revolution.” It started with the internet of documents (e-mail and web pages) in the 1990s, which connected hundreds of millions of people in new ways. (Remember how skeptical we were then about how this was all going to work?) This technology then advanced to the internet of commerce (on-line shopping), increasing the number of people on the internet by a factor of 3 to 5. Then, along came the internet of people (social media), connecting more than half of humanity. And now, the next wave of technology is connecting people to the internet of things – 20 to 30 billion things – over the next few years (Agarwal, 2018).
So, what are these “things” we are connecting to? They fall into several buckets that include:
The home front. These are gadgets that make your life easier or more interesting. These gadgets include connecting things like thermostats, garage doors, cameras, doorbells, medical home devices, and even your refrigerator – allowing you to do an inventory of your food while at the grocery store. (How many phone calls will that save you?) And, how about the iRobot, the ultimate robot mop – we so love this one! Then there’s the sex robot named Harmony that interacts in very life-like ways. (Don’t ask!)
The public sphere. These are things that help us manage and optimize our city services, such as transportation, water supply, waste management, and law enforcement. A good example is the autonomous vehicle. For some of us, giving up that much control just might send us into anaphylactic shock! Imagine the impact on the 4-5 million people who work in this sector such as taxi, truck, and bus drivers if autonomous driving becomes the norm.
The industrial sector. Artificial Intelligence (AI) is used to address many business problems and gain efficiencies. Connections to equipment can identify an asset’s remaining useful life. Safety and security are enhanced using facial recognition. Algorithms can predict potential system failures. Generative design helps humans consider important factors such as preferred materials, purchasing decisions, manufacturing capacity, product variances, and supply chain status. (Hmmm, where was this when we were looking for PPE?)
Military sector. The military has many non-combat uses of AI to enhance planning and training through simulation, with ongoing data analysis to inform and improve decision-making and reaction time. On the more ominous side, there is a growing concern about the development of “non-human combatants” or “killer robots” as autonomous weapons in the future. Beginning to sound like one of your favorite sci-fi movies? Just imagine the ethical and safety concerns that come with this.
These billions of connected devices will generate massive changes in our world. The implications for leaders are significant. Proactive consideration of ethical guidelines, standards and strategies are essential. Front and center will be strategies that promote the improvement of education, training, and reskilling of our workers.
Artificial Intelligence (AI), Machine Learning and Deep Learning. AI is the broad umbrella term for computational technology that works and reacts in humanlike ways to address or solve specific problems and tasks. It is the science of training systems to emulate specific human tasks through learning and automation.
Machines that learn from experience, can adjust to new inputs and perform specific tasks such as pattern recognition, data anomaly identification, image and video analytics, and even find and discern context within unstructured data (Agarwal, 2018).
AI supports many of our creative and work activities in daily life. Examples include applications that help us navigate, prepare our taxes, write music, write more effectively, customize and target our shopping and marketing. And let’s not forget our digital personal assistant support from Siri, Amazon Echo, and Cortana.
And with the growth of our technology comes the explosion of data (Big Data) accumulated from our many sources – social media, internet, search engines, electronic devices and things – to analyze and provide additional value. The most common and efficient way to do this is through the AI approach of machine learning. Machine learning is a linear approach to solving problems using sets of algorithms created by humans. These algorithms are a set of pre-determined specifications, instructions and trends that guide the machine through the process. While fast and efficient, if variables are introduced that were not built into the specifications, the analysis can be negatively impacted.
A subset of machine learning is deep learning. This is an advanced form of machine learning in which the machine can cope and adapt to the constant feedback coming at it, improving the model as it goes. It learns through trial-and-error methods up to a few hundred million iterations! It can, therefore, handle a larger collection of problems and complexity, and with greater ease and efficiency.
There are significant challenges that come with the ongoing development of AI. We present four of them here:
Workforce. AI advancement in the short-term could lead to the loss of jobs due to AI-driven automation. This potentially will increase wealth for some and financial risks for many. A study released by Oxford University in 2013 found that 47 percent of the U.S. employment will be impacted by AI technologies (Subcommittee on Information Technology Committee on Oversight and Government Reform, 2018). Leaders can anticipate job loss and job change early and invest in effective strategies for improving education, training, and reskilling of a targeted workforce.
Privacy. As more and more data are collected, analyzed, and personalized the risk of privacy breeches and data mismanagement rises. Privacy breeches can come from many places, intentional and not, to include hackers, mismanagement of data collected, misuse of data for another purpose, misuse of data from voice-activated systems, etc. It’s important for leaders to know and review privacy laws and regulations and build a strong defense within their organizations. How many of you have been hacked or been informed of a privacy breech with your credit card? Sad that this is becoming commonplace. Protecting our information should be a top priority.
Bias. AI raises fundamental questions about accountability, due process, and fairness. AI technologies rely on computer algorithms and data inputs created by humans. Humans have biases, both implicit and explicit, that can get coded into programs. Examples include algorithms for hiring, promotion, medical treatment, and school admissions that can have built-in biases that produce coded discrimination and biased results. Addressing biases and potential biases in AI systems will necessitate transparency when those systems are used to make consequential decisions about individuals. Algorithms need to be available and inspectable, opening up the proprietary black box to the extent legally possible to those who are expected to engage with the technology (Babic, Chen, Evgeniou & Fayard, 2020).
Malicious Use. AI has the capability of helping and harming. Two areas of malicious use for leaders to be aware of are: physical security (examples include hacking into medical devices or autonomous vehicles), and digital security (creating more effective, targeted, and difficult-to-attribute hacks). As leaders, it is important to have safeguards in place to protect the people and the business.
Vocal biomarkers. Amazing work is going on by a few MIT Lincoln Laboratory researchers who are detecting changes in COVID-19 patients that are too subtle for the people themselves to notice (Quatieri, Talkar & Palmer, in press). The subjects of this very small research sample were five celebrities or broadcast hosts that became COVID positive. Using their vocal recordings from press conferences and TV interviews on YouTube, Instagram, and Twitter (yes, this is a great example of AI and privacy issues), researchers were able to study the recordings pre-COVID-19 (before exposure) and post-COVID-19 (after the person became positive, but was asymptomatic).
A biomarker is a tangible physical substance or quality that can be measured. Its presence is indicative of some phenomenon such as disease, infection, or environmental exposure. Vocal biomarkers come from the ability to measure the qualities and coordination of a human’s vocal system – their loudness, pitch, steadiness, and resonance of their voice (Quatieri, Talkar & Palmer).
The researchers’ hypothesis was that people with COVID-19 inflammation have muscles across the vocal system that become overly coupled (stick together), resulting in a less complex movement that is measurable. In other words, the infection disrupts the movement of the muscles across the respiratory, laryngeal, and articulatory systems, becoming detectable to researchers, but not necessarily to the person infected. Indeed, this is what they found! How cool is this? This is so much better than having a very ginormous Q-tip stuck up your nose to goodness-knows-where!!!
The implications for this technology following further research are many. One of our new tools for virus detection may be vocal screening through mobile apps. Imagine if we could accurately discern other diseases and conditions (e.g., depression, Alzheimer’s, and Parkinson’s, to name a few) early on through vocal biomarkers, and intervene earlier to improve our health and wellbeing (Mor, 2020). This is when technology is truly a wonderful thing!
Woebot. According to the Anxiety and Depression Association of America, depression effects 18 percent of the population over 18 years old, making it one of the leading causes of disability in the country. Woebot is an intelligent software chatbot application (a computerized conversational human-like agent) that uses AI for mental health counseling. The counseling method is Cognitive Behavioral Therapy (CBT), a well-established psychotherapy used to help people change the thoughts, feelings, and behaviors that are causing them problems. It’s not intended as a replacement for traditional methods of therapy, but is a new tool in the toolbox for fighting depression. In fact, Woebot has been shown to significantly decrease symptoms of depression (Brodwin, 2018). We think we could all use a little bit of Woebot!!! Maybe make it part of our COVID-Rescue!
Titter Time: oxYmoron
One final oxymoron: Always remember you’re unique…just like everyone else!
References
Agarwal, P. (2018, November-December). Public administration challenges in the world of AI and bots. Public Administration Review, 917-921.
Anxiety and Depression Association of America. Retrieved from https://adaa.org/about-adaa/press-room/facts-statistics
Babic, B., Chen, D., Evgeniou, T. & Farard, A. (2020, July-August). A better way to onboard AI: Understand it as a tool to assist rather than replace people. Harvard Business Review, 57-65.
Brodwin, E. (2018, January). A Stanford researcher is pioneering a dramatic shift in how we treat depression — and you can try her new tool right now. Business Insider.com. Retrieved from https://www.businessinsider.com/stanford-therapy-chatbot-app-depression-anxiety-woebot-2018-1
Oxymoron (2020). Merriam-Webser.com. Retrieved from https://www.merriam-webster.com/dictionary/oxymoron
Quatieri, T., Talkar, T. & Palmer, J. (in press) A framework for biomarkers of COVID-19 based on coordination of speech-production subsystems. IEEE Open Journal of Engineering in Medicine and Biology. Retrieved from https://ieeexplore.ieee.org/ielx7/8782705/8937520/09103574.pdf?tp=&arnumber=9103574&isnumber=8937520&ref=
Reynolds, P. (2018). The Word Collector. New York: Orchard Books.
Subcommittee on Information Technology Committee on Oversight and Government Reform U.S. House of Representatives (September 2018). Rise of the Machines Artificial Intelligence and its Growing Impact on U.S. Policy.
Rao, V. (2018, April 4). Difference between AI, Machine Learning and Deep Learning. Technotification. Retrieved from https://www.technotification.com/2018/04/deep-learning-machine-learning-and-ai.html#:~:text=Deep%20Learning%20and%20Machine%20Learning%20are%20words%20that,differentiate%20the%20three%20starting%20off%20with%20Artificial%20Intelligence
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