Sep
What It Means to Choose or Decide In The Age of AI
jerry9789 0 comments artificial intelligence, Burning Questions
Longstanding Concerns Over AI
From an open letter endorsed by tech leaders like Elon Musk and Steve Wozniak which proposed a six-month pause on AI development to Henry Kissinger co-writing a book on the pitfalls of unchecked, self-learning machines, it may come as no surprise that AI’s mainstream rise comes with its own share of caution and warnings. But these worries didn’t pop up with the sudden popularity of AI apps like ChatGPT; rather, concerns over AI’s influence have existed decades long before, expressed even by one of its early researchers, Joseph Weizenbaum.
ELIZA
In his book Computer Power and Human Reason: From Judgment to Calculation (1976), Weizenbaum recounted how he gradually transitioned from exalting the advancement of computer technology to a cautionary, philosophical outlook on machines imitating human behavior. As encapsulated in a 1996 review of his book by Amy Stout, Weizenbaum created a natural-language processing system he called ELIZA which is capable of conversing in a human-like fashion. When ELIZA began to be considered by psychiatrists for human therapy and his own secretary interacted with it too personally for Weizenbaum’s comfort, it led him to start pondering philosophically on what would be lost when aspects of humanity are compromised for production and efficiency.
Copyright chenspec (Pixabay)
The Importance of Human Intelligence
Weizenbaum posits that human intelligence can’t be simply measured nor can it be restricted by rationality. Human intelligence isn’t just scientific as it is also artistic and creative. He remarked with the following on what a monopoly of scientific approach would stand for, “We can count, but we are rapidly forgetting how to say what is worth counting and why.”
Weizenbaum’s ambivalence towards computer technology is further supported by the distinction he made between deciding and choosing; a computer can make decisions based on its calculation and programming but it can not ultimately choose since that requires judgment which is capable of factoring in emotions, values, and experience. Choice fundamentally is a human quality. Thus, we shouldn’t leave the most important decisions to be made for us by machines but rather, resolve matters from a perspective of choice and human understanding.
AI and Human Intelligence in Market Research
In the field of market research, AI is being utilized to analyze a multitude of data to produce accurate and actionable results or insights. One such example is deep learning models which, as Health IT Analytics explains, filter data through a cascade of multiple layers. Each successive layer improves its result by using or “learning” from the output of the previous one. This means the more data deep learning models process, the more accurate the results they provide thanks to the continuing refinement of their ability to correlate and connect information.
While you can depend on the accuracy of AI-generated results, Cascade Strategies takes it one step further by applying a high level of human thinking. This allows Cascade Strategies to interpret and unravel insights a machine would’ve otherwise missed because it can only decide, not choose.
Take a look at the market research project we performed for HP to help create a new marketing campaign. As part of our efforts, we chose to employ very perceptive researchers to spend time with worldwide HP engineers as well as engineers from other companies.
This resulted in our researchers discovering that HP engineers showed greater qualities of “mentorship” than other engineers. Yes, conducting their own technical work was important but just as significant for them was the opportunity to impart to others, especially younger people, what they were doing and why what they were doing was important. This deeper level of understanding led the way for a different approach to expressing the meaning of the HP brand for people and ultimately resulted in the award-winning and profitable “Mentor” campaign.
If you’re tired of the hype about AI-generated market research results and would like more thoughtful and original solutions for your brand, choose the high level of intuitive, interpretive, and synthesis-building thinking Cascade Strategies brings to the table. Please visit https://cascadestrategies.com/ to learn more about Cascade Strategies and more examples of our better thinking for clients.
Aug
How Can Healthcare Companies Identify Who Needs Remediation Programs?
jerry9789 0 comments artificial intelligence, Burning Questions
What Is Remediation?
The Cambridge Dictionary defines remediation as “the process of improving or correcting a situation.” Remediation programs are commonly employed in teaching and education wherein they address learning gaps by reteaching basic skills with a focus on core areas like reading and math. And as pointed out in an understood.org article, remedial programs are expanding in many places in our post-COVID 19 world.
In healthcare, there’s a wide range of remediation programs, or “remedial care,” diversified based on their end goal which may include smoking cessation, anti-obesity, weight reduction, diet improvement, exercise, heart-healthy living, alcoholism treatment, drug treatment, and more. But how do you identify the people who need remedial care the most?
Who Needs Remediation?
You might say you can tell who needs remedial care by just looking at the physical aspect of the prospective patient, but this is a shortsighted answer to the question. And what about those who need remedial care for a heart-healthy lifestyle? Surely you can’t tell a likely candidate for this remediation program with just one look alone.
It goes deeper than that. What if you, a healthcare representative, could only devote remedial care to a select few individuals given limited resources and time but you want to make sure that the whole remediation program is successful by achieving its intended goals? Just imagine all that time, effort and resources spent only for the patient to relapse back into their old ways not too long after program completion- or even in the middle of the remediation process itself.
Deep Learning and Remediation
This is where deep learning comes in. Also known as hierarchical learning or deep structured learning, Health IT Analytics defines deep learning as a type of machine learning that uses a layered algorithmic architecture to analyze data. In deep learning models, data is filtered through a cascade of multiple layers, with each successive layer using the output from the previous one to inform its results. Deep learning models can become more and more accurate as they process more data, essentially learning from previous results to refine their ability to make correlations and connections.
Deep learning models handle and process huge volumes of complex data through multi-layered analytics to provide fast, accurate, and actionable results or insights. When applied to the scenario we mentioned beforehand, deep learning filters through that multitude of patient data and prioritizes those who need remedial care the most.
You can also align its findings to effectively identify individuals who will not only return monetary value to your healthcare brand, but at the same time are most likely to “engage” or participate in programs offered by your company, such as wellness, diet, fitness or exercise. They can also be the best people to commit to avoiding poor lifestyle choices, such as overeating, smoking, and alcohol, helping guarantee the success of the remediation program.
With a combination of three decades of market research experience and conscientious use of AI, Cascade Strategies has been helping healthcare organizations develop advanced models to handle, filter and identify the likeliest of candidates for their program purposes. Cascade Strategies helps industry professionals not only recognize their ideal customers but also reach out to them with some of the most effective and award-winning marketing campaigns, thanks to our array of services such as Brand Development Research and Segmentation Studies. To see more examples of how we help leading worldwide companies achieve their goals, please visit our website.
Here are some of our suggestions for further reading on deep learning and healthcare:
https://builtin.com/artificial-intelligence/machine-learning-healthcare
https://research.aimultiple.com/deep-learning-in-healthcare/
https://healthitanalytics.com/features/types-of-deep-learning-their-uses-in-healthcare
info@cascadestrategies.com
2032 Newport Way NW,
Issaquah, WA 98027
(425) 677-7430