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Reflections Blog

Optimization, Oppression, and Optimism In The AI Age

3/30/2023

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Future of Work, Opinion
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Resistance is futile. The artificial intelligence revolution is underway. All hail our robot overlords.
Too much to start off a piece on the future of work? Perhaps, but many people have been feeling this way over the last few months. 
Late Fall 2022 was rocked by the public release of ChatGPT, an online chat bot from the company OpenAI that leverages large language models to generate predictive text "responses" to user-entered prompts. The technology has captured the public's attention with over 100 million users of the product within 2 months of launch, the fastest user uptake of an internet-based application/product in history.  ​
And on March 14, 2023, OpenAI announced the launch of the even more powerful GPT-4, which they claim can score in the 85th or higher percentile on the LSAT, SAT, and AP Biology exam. During a livestream demo of the platform (which garnered over 1.4 million views in less than 18 hours), the company showed the power of this next version of their technology, which can perform a range of functions from assisting with writing and troubleshooting computer code to analyzing an image. The demo also highlighted how GPT-4 can take a human-sketched and written design and create website html code or advise on one's taxes (by understanding and acting on the thousand-page US tax code). The range of capabilities and versatility of this model's output is quite astounding! 
Understandably, the release and promotion of ChatGPT, GPT-4, and other "generative artificial intelligence (AI)" products (Meta launched LLaMA in late February 2023 and Google's Bard and Anthropic's Claude both launched in March 2023) is being met with both awe and fear. There is a sense that the current "AI arms race" between companies and governments could lead to the technology outrunning needed safeguards and ethnical discussions around its use. The rapid pace of advancement of this technology has left many to make the case we should slow down on accelerating its deployment, including a statement signed by over 1,000 technology and business leaders urging caution in growing the size of large language models until they are better understood and more regulatory and security guardrails are put in place. 
Even the founder and CEO of OpenAI, Sam Altman, is unsure where this new technology will lead. Though he acknowledges generative AI will displace some human work in the near future, he is hopeful that it will ultimately create better jobs and more fulfillment for humanity. His recent interview with ABC News is shared below and you can watch a longer interview with him and Open AI's Chief Technology Officer here. ​
Versatile AI in a "Black Box"
The consulting firm Gartner has published a report on use cases for generative AI in a variety of industries and sectors. In their report, they highlight how AI could be used to assist in drug design and materials science research, including optimizing the design of various industrial components or semiconductors to maximize a particular use case or efficiency target. In addition, the investment firm ARK's 2023 "Big Ideas" report has made the case that advances in AI are the key catalysts to advancing the development of a variety of innovations from precision therapeutics to robotics and autonomous transportation. 
​The ability for large language models to ingest a large amount of data and have their parameters weighted to achieve certain outputs makes them incredibly versatile and powerful. One interesting twist, however, is that even the developers of generative AI models and interfaces aren't entirely sure how they produce their output. The means by which these models produce data often involves complex neural networks and reinforcement learning approaches that result in very complex "routes" from input to output that are not initiative for a human observer to understand (though some researchers are working to make these neural networks more explainable). Ethical questions have been rightly raised regarding whether bias in these models or the data they were trained on could result in flawed output. 
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And if this output becomes increasingly relied on to aid in consequential decisions (think AI-assisted mortgage determinations), the inability to understand the basis from which the model generates output is problematic. It brings up the question of whether all output, products, or decisions that affect society should be rendered by a model that weights various pieces of input through an incredibly large neural network with hundreds of billions of parameters.
​Can all our problems be solved with more data and more computing power? 
Spoiler alert: No.
​​
"Everyone will have their own white collar personal assistant."
Microsoft founder Bill Gates said as much in a recent blog post about the power and potential of AI. It is not difficult to see how generative AI could be very helpful as an assistant to humans, helping them be more creative and productive.
Thanks to backing of OpenAI by Microsoft, ChatGPT-like technology is being integrated into the Microsoft 365 Suite of business software (Word, Excel, Powerpoint; which they have dubbed "Copilot") and their Bing search product. And recently Zoom announced an AI integration in their video meeting platform. AI as a productivity assistant is upon us. 
And who wouldn't be excited for the day when Outlook or Gmail offers to author responses to your 50+ un-replied-to work emails in a matter of minutes? Let AI handle the boring, administrative things while you focus on other more pressing matters. Though, this does not speak to the complexities around how the human reading your message on the other end feels about it, especially if they know it was written by AI. One could quickly see that this results in some weird future where human beings are not "in the loop" of these digital communications at all. AI written content being "read" by AI models to be responded to with AI and on and on it goes....In this infinite communication loop, what is the point of having a human involved at all? Does someone need to interpret the exchange and act? Could that one day be an AI decision maker (or human decision maker "assisted" by AI)?
​This quick little thought experience brings up the philosophical point around what are human beings for in a knowledge economy that my one day be driven predominantly by AI that is more efficient and effective at a range of data-based tasks? 
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Productivity & Progress
Our modern, 21st-Century American economy is quite fixated on productivity. In fact, many news outlets lament a recent drop in worker productivity, defined by the Bureau of Labor and Statistics as how much total economywide income is generated (i.e., for workers, business owners, landlords, and everybody else together) in an average hour of work. Despite the recent decline in this metric in the past few years, it is inarguable that at a global scale, worker productivity has increased greatly over the past 50 years along with our technological progress.  ​
In theory, increased productivity should lead to increased prosperity, right? This is only true if productivity gains are shared across a society. However, data show that the gap between the average workers' pay and overall productivity in the United States has grown dramatically since the 1980s. While productivity from 1979 until 2021 grew by 64%+, average compensation grew at only a 17% pace. In short, the "returns" generated from increased productivity have not been shared with the average worker in America. This fact shocks no one and reinforces the argument that inequality (in the US and beyond) has accelerated since the early 1980s. 
One open question from these data, then, is whether the gains in productivity and efficiency to come from generative AI will be shared across society or concentrated in the hands of a few? The fact that OpenAI has "open" in its name and has, at least for now, made its ChatGPT technology available to anyone perhaps signals a more egalitarian approach to sharing this technology more equally than many that came before it. It is important to note, though, that while the interface is "open" the source code and details behind the data used to train the model ​are kept carefully under wraps. 
Companies like OpenAI suggest that these GPT technologies will make many workers more productive and efficient. The fact that anyone can access and use ChatGPT would suggest that anyone and everyone can become more productive by using it. This sounds like a great thing but how much can human performance be optimized? If we are talking about optimization, is that something better left to the machines and algorithms anyway?  ​
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​Job Automation 
Studies investigating the effects of robotic automation on industrial and manufacturing jobs found evidence that the deployment of industrial robots reduced both the number of human workers and their wages in these industries. Then came the dawn of generative AI that demonstrated automation of creative and knowledge work, generating stunning visual images and often clever and compelling written words in a matter of a few seconds. This advancement is so new we cannot yet measure its effects but one can easily see that "automation" can replace more than routine, manual manufacturing work.
Just this month, OpenAI and researchers at the University of Pennsylvania released a pre-print (not peer-reviewed) publication titled "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models". In this study, they assessed occupations based on their correspondence with the current capabilities of the Generative Pre-trained Transformer (GPT) models behind technology like ChatGPT and GPT-4. They found that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. They go on to state: "The influence spans all wage levels, with higher-income jobs potentially facing greater exposure." 
And just this week (March 26, 2023), Goldman Sachs's Economics Research released a report predicting that two-thirds of US jobs are exposed to automation by AI and going on to state that it believes ~7% of current US jobs could be replaced by AI (which, based on a current US labor force size of 166 million, equates to 11.5+ million people - more than the population of the state of Georgia). The positions at greatest risk according to their report: administrative support, legal positions, and architecture and engineering jobs. Meanwhile, they found the jobs with the lowest exposure to AI automation included those in cleaning and maintenance, installation and repair, and construction. On a more positive note, the Goldman Sachs report believes AI could increase the total value of goods and services created worldwide by 7%. So, while some humans may be rendered redundant by AI, overall value in the economy could be increased, raising the question we posed earlier: who will see the economic benefits of AI and who will bear the costs?
The findings of these studies are perhaps not all that surprising as the world gets a better sense of what GPTs can accomplish. In some ways, administrative and knowledge work is the most automatable, even if there needs to be large advances in current technology to get to a (dystopian?) future where AI has replaced all ​human knowledge work.
The seeds of societal change planted during the COVID-19 pandemic, specifically a demonstration that much knowledge work can be performed remotely, may have also accelerated the ultimate replacement of this type of work by AI. Many white-collar technology workers have resisted the return to office work over the past few years, removing themselves from physical interactions with co-workers and supervisors. In many ways your work output can be dissociated from your humanity when it is transmitted to your employer and customers via electronic means. The employer and coworkers often don't experience a remote worker as fully human but rather pixels on a Zoom screen or text messages on Slack. In a few years time, AI may be able to conjure a digital collection of pixels that mimic a "real" human over video that we won't even be able to tell the difference. For years we have been increasingly dissociating the physical world from the digital one and, thus, replacing pieces of the physical world (ie, human employees) with digital options (AI workers) seems inevitable. The bigger question is how much replacement is possible and ethical.  
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The Future of Work is More Human
As AI becomes increasingly better at producing digital output, including images generated from programs like DALL-E or DreamStudio or computer code from Copilot, it is important to remember that there is still a physical world with many needs and problems that AI cannot yet act on effectively. Some jobs and tasks currently performed by humans will be very difficult if not impossible to automate away. Human skills and professions that emphasize physical interaction and engagement with objects in the world will remain essential as long as we live in a physical world with others (but the creation of a functional metaverse could change that). One could imagine a not-too-distant future where the skilled trades (which are already seeing a renewed interest and level of appreciation in younger generations) gain even more respect from society. A robot is not going to fix your plumbing or electrical issue any time soon. Construction work is another example of work that seems un-automatable.​
Ironically, tasks performed by the "trade professions" that many may view as "routine" but require presence in and manipulation of the physical world are much harder to automate than many futurists would have predicted a few decades ago. And perfecting the autonomous car has been exceedingly difficult even with vast amounts of resources devoted to the effort. Maybe those truck driver jobs are more safe than initially thought? Bottom line: acting on and operating in the physical world is really hard for current AI. ​
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Another large category of work that I think will be very difficult for AI to replace is in the caring fields - think healthcare, childcare, counseling, and even teaching (especially young children). These caring professions involve interacting with human beings and even if one theoretically could automate this type of work, I do not think humanity would be very keen on entrusting the care of its children, elderly, and sick to robots and automated systems anytime soon. Knowing that another human being cares about you and your loved one's well-being is critically important and while generative AI can seem human and caring, it is important to remember that this technology does not have intentions or motives. Current AI literally does not "care" about anything. 
So, while the late 20th Century economy valued data science and computer skills, the future of work will reward those that are handy and humane. Both technical skills that center around operating in the physical world and interpersonal skills that help one work with, understand, and assist other human beings will become increasingly important to thrive in the future. This shift in what types of work is valued could have massive societal implications. 
Richard Reeves from the Brookings Institute has made a case for the growth of HEAL jobs: health, education, administration, and literacy and the fact that many of these professions are only ~25% occupied by men. He speculates that many men have shunned occupations like teaching and social work due to low pay but as labor shortages in these "hands-on" fields continue, demand may push up compensation. This may be even more true as AI replaces careers in software, computer science (some startup companies have already indicated they are using GPT-4 technology to reduce the need to pay human coders for services), finance (a recent pre-print suggests ChatGPT can help you pick a diversified investment portfolio), and others which have been overwhelming held by well compensated men. It is possible that the rise of AI will signal an increased societal importance placed on the HEAL fields. These professions and those of the skilled trades have always been critical to a functional society and it may take the rise of AI for more people to appreciate that point. I think, in the end, that will be a good thing. 
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A Post-Work World?
​Even if the demand for HEAL professions and skilled trades rises in the years to come, the labor force will be forever changed as a result of generative AI technology and sooner than many may think. An interesting wrinkle in all of this is that if it is the knowledge workers whose occupations disappear will there be a larger push by the educated "elite" to support the unemployed and perhaps push for a universal basic income? Sadly, when blue collar workers in the US lost their jobs as a result of deindustrialization and globalization in the 1980s and 1990s many seemed unconcerned. When disruptive AI comes for the careers of journalists, computer programmers, and marketers, those influential individuals will definitely make some noise. Maybe they will be loud enough to help reshape our society to be less focused on work and wages than we have come to understand them? 
While a world without "work" seems almost unimaginable, it is important to point out that this mostly implies a world without humans performing some work. There may come a time soon when work that is tedious, laborious, and often undesirable by people will be replaced by automation, AI, and machines. This would, in effect, free people from performing these tasks. Instead, they may be able to focus instead on activities they enjoy (hobbies or creative pursuits) and that, at least in theory, can be good for society writ large - volunteering, community engagement, and care-giving, among others. 
​Image generation programs like DALL-E (from OpenAI) and DreamStudio (from Stability AI) and the advancement of AI video creation tools would suggest that creative work will also be vulnerable to automation. Alternatively, some have argued these technologies will make humans more creative. Though, we may need less creative types who are paid for their services if AI is doing most of the heavy lifting. Imagine a future where AI can create a unique, customized movie generated to your exact tastes. Would that lead to us amusing ourselves to death or being more glued to our screens?
​In a future of endless content and diversion, will we still seek to make an impact on the world?
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Image generated via DreamStudio using prompt: "robot running past a man in a race, photo art, HQ, 4k"
Work Worth Doing
Far and away the best prize that life has to offer is the chance to work hard at work worth doing. - Theodore Roosevelt
Teddy Roosevelt's quote highlights a big existential question we face as AI is increasingly able to do more of our work: What is work for?
It could be argued that many human beings engage in "work" for meaning. They want to justify to themselves that their life has purpose and impact and this is often reflected in the work they do. The definition of "work" in the future may change, though. While today we think of work as an occupation one performs in exchange for wages to subsist on, perhaps the future of work is work that is more focused on contribution to the betterment of others that is decoupled from any monetary compensation? This idea seems very foreign to many but who says human work has to be about delivering measurable economic value? 
The increasing efficiency and productivity of AI suggests we shouldn't try to compete there. Let AI do what it is good at: optimizing parameters, testing models, and generating creative content. Freed from the need to "produce," humans may be able to think more about what they really care about and how they want to live their lives. Automation and the end of work as we know it may free us from a hyper-capitalist society where all too often our value is measured by what we produce. Instead, a future where AI worries about industrial and knowledge production at scale could free people from thinking of themselves as economic assets or liabilities on society. Rather, we could focus more on what we love to do rather than what we have to do.
This could lead to a place where everyone pursues their passions. We might also choose to focus on tasks and "work" that we know AI can "do better" but that we find fulfillment in doing ourselves. Steven Hales touches on this idea nicely in a recent piece entitled "AI and the Transformation of the Human Spirit" where he makes the point that successful authors still write despite their financial position and technology has made it such that most people don't need to bike 5 miles along an open road on the weekend (a Peloton bike is so much more efficient) or mountain climb but they do it anyway. I can't say it better than he does in the quote below:
When AI lifts the burden of working out our own thoughts, it is then that we must decide what kinds of creatures we wish to be, and what kinds of lives of value we can fashion for ourselves. What do we want to know, to understand, to be able to accomplish with our time on Earth? That is far from the question of what we will cheat on and pretend to know to get some scrap of parchment. What achievements do we hope for? Knowledge is a kind of achievement, and the development of an ability to gain it is more than AI can provide. GPT-5 may prove to be a better writer than I am, but it cannot make me a great writer. If that is something I desire, I must figure it out for myself. - Steven Hales
In addition, free from the need to produce we might be able to re-engage with our fellow humans and be able to have deep and meaningful conversations with others to find common ground and aspirations for our society. To realize that we are all human and deserve basic respect, dignity, and support. We could work to repair our societal and community institutions like schools, civic groups, government agencies, legislative bodies, and so much more with all this new-found time. ​
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In an ironic way, the rise of AI could bring us closer together as a species by better understanding what it means to be human. And while in the interim generative AI has the potential to produce more misinformation and destructive content, we are increasingly realizing that a negative digital world is not what we want. I believe much online negativity and tribalism has been fueled by fear of the world's resources and opportunities being zero-sum...that there is not enough success or money or power to go around. In a future where AI has freed us of the need to "produce to survive" we may be able to evolve past the scarcity mindset that has been a reality for our species from the beginning. It will be a paradigm shift and certainly take time but I believe in the end this technology will produce more abundance for the human race. We will still need to do the human work of engaging with and supporting our fellow man and employing the ethical use of AI in our society to realize the benefits of this optimistic future, together.  
Steven Hales concludes "AI and the Transformation of the Human Spirit" with something we all should think more about in this brave new world we are entering.
We are living in a time of change regarding the very meaning of how a human life should go. Instead of passively sleepwalking into that future, this is our chance to see that the sea, our sea, lies open again, and that we can embrace with gratitude and amazement the opportunity to freely think about what we truly value and why. This, at least, is something AI cannot do for us. What it is to lead a meaningful life is something we must decide for ourselves.
​- Steven Hales
The Future is What We Make It
One fact that I think gets lost in the wonder that is generative AI is that is a product of virtually all of us. Yes, programmers and computer scientists created the neural networks and reinforcement learning models that lead to the ability for the AI to generate output. But it was our collective knowledge contribution via the internet that fed these models with vast amounts of data generated by billions of humans over thousands of years sharing their art, ideas, knowledge, worries, fantasies, hopes, and dreams with the world. Out of these models has come something "human-like" but not necessarily human. We must remember the difference. While generative AI can produce wonders and probably will lead to more productive generation of media to entertain us and knowledge to empower us, it can also be leveraged by bad actors to turbocharge our fears and anxieties. 
This technology is, in a way, a fun-house mirror for our humanity. It reflects back at us surprising, scary, and wonderous things. It could enslave us in a future where we are subjects of the black-box algorithm that strives for efficiency and productivity whatever the costs. Or, it could make us more human and free us from the drudgery of many tasks, leaving us more time to focus on helping and caring for others and being in community with our fellow man.
What will we do with the time AI may give back to us?
​How will we be responsible stewards of a technology capable of immense constructive and destructive impact as it continually improves over the coming months and years?  ​
In the end, we will get out of this technology whatever we collectively feel is most valued and important to us. I hope we choose humanity over optimization and oppression.
​Our future depends on it.
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Dystopian future city image generated with DreamStudio
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Utopian future city image generated with DreamStudio
More from the Blog:
  • To Be Rather Than To Seem
  • The End of Work as We Know It: How an increasingly automated world will change everything (from December 2019)
  • Precarity, Competition, and Innovation: How economic systems and societal structures share our future
For Further Reading
  • AI Is Like … Nuclear Weapons? The new technology is beyond comparison.
  • Big Ideas 2023 from ARK Invest
  • What Have Humans Just Unleashed?
  • Welcome to the Big Blur: Thanks to AI, every written word now comes with a question.
  • Why All the ChatGPT Predictions Are Bogus
  • The Economics of AI
  • The case for slowing down AI
  • Preparing for the (Non-Existent?) Future of Work (Brookings Institute Report)
  • Robots and Jobs: Evidence from US Labor Markets (NBER paper from 2017)
  • Post-work: The radical idea of a world without jobs
  • The Crisis of Social Reproduction and The End of Work
  • ​Redistributive Solidarity? Exploring the Utopian Potential of Unconditional Basic Income
  • Enjoy the Singularity: How to Be Optimistic About the Future
  • How to be a leader in an AI-powered world
Recent Pre-Print and Other Publications on GPTs
  • Predictability and Surprise in Large Generative Models
  • GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
  • Sparks of Artificial General Intelligence: Early experiments with GPT-4
  • Theory of Mind May Have Spontaneously Emerged in Large Language Models
  • GPT-4 System Card
Book Recommendations
  • Broken: How our social systems are failing us and how we can fix them​​
  • ​Futureproof: 9 Rules for Humans in the Age of Automation
Listen to:
  • Andrew Yang's Forward Podcast interview with Kevin Roose on "Futureproofing Your Career in the Age of ChatGPT"
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The End of Work as We Know It: How an Increasingly Automated World Will Change Everything

12/16/2019

0 Comments

 
Career & Professional Development, Future of Work, Opinion
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Updates, February 2021:
​I encourage anyone interested in the future of workforce development and training to read this excellent article published in Forbes: 
Human Capital Era Reality: The Skills Gap May Never Close

For other relevant readings on the future of work, see:
Future of Work: Insights for 2021 and Beyond (Milken Institute)
The Future of Work After COVID-19 (McKinsey Global Institute) 
​Preparing for the Future of Work (World Economic Forum)
World Economic Forum's Future of Jobs Report 2020

In a few short weeks we will be entering a new decade.

What will the 2020s hold? Technological advances continue to take shape at a blistering pace and will have effects on nearly all aspects of our lives. While most of these tech advances will be a clear benefit (improved healthcare, faster connectivity with 5G wireless, and greater access to online learning), others offer a potentially existential challenge to something most of us do for more than a third of our lives: work.  

Work in the Age of AI
The nature of work is changing rapidly. Automation and artificial intelligence (AI) are allowing machines and computer programs to take on an extraordinary variety of tasks.
We think of this as a blue-collar issue with robots now doing most of the assembly at automobile plants across the world and autonomous vehicles coming to take truckers jobs soon. The truth is, though, very few jobs are safe from automation as many jobs are quite routine and skills-based.

Any job requiring technical skill alone may eventually be subsumed by advances in AI. This leads to the important question of how does one prepare for this sea change? How can you make yourself un-replaceable by a machine?
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A 2017 McKinsey Global institute study concluded up to 800 million workers worldwide could lose their jobs to automation by 2030.

Workforce Transitions in a Time of Automation (McKinsey, Dec 2017)
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What Skills Will Be Valued in Future Work?
While it seems logical to assume that increases in AI would necessitate increased training in technical skills like computer programming, coding, engineering, or other scientific fields, the fact of the matter is that those technical skills themselves may be unsafe from the evolving capability of our machines.
Specifically, a report by the Brookings Institute where job descriptions were compared against AI-related-patents (see paper by Michael Webb at Stanford University for empirical details) found that white collar, data-intensive jobs in business, technology, engineering, and science are at greatest risk of being impacted by AI. The report quantified impact as having job duties that could be subsumed by coming AI advances. This doesn't mean AI would replace the job completely...in fact, AI may just supplement work in fields such as finance and scientific research. What is clear, though, is that only being good at a skill or technique that eventually can be automated is NOT a winning strategy. 


​"AI exposure will likely lower wages and lead to job replacement if human workers can no longer bring "extra value" that AI can't."
- Mark Muro, Senior Fellow & Lead Author of the Brookings report
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Transferable, Human-Centered Skills are Irreplaceable (for now)
What will be in demand for the foreseeable future are transferable skills. These types of skills aren't technical but rather refer to intra- and inter-personal skills that we use to get our work done efficiently, by ourselves or with others.
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LinkedIn found these 5 transferable skills to be most in demand in 2019 (based on job postings on the platform):
5. Time Management
4. Adaptability
3. Collaboration
2. Persuasion
1. Creativity
In addition, as more and more employees become tech-savvy, backgrounds that give employees perspective — historical, global, or otherwise — will become increasingly valuable. We will need to increasingly think about how our machines and interfaces are designed to interact with humans.

​Currently, machines cannot think like human beings. The AI we have today is so-called "narrow AI,” performing objective functions using data-trained models. The AI from science fiction movies where machines have human-like intelligence, so-called "artificial general intelligence," is far from a reality and may never be achieved. So, we will need people to serve as the interpretational bridge between AI and society and back again...to insert humanity into our machines and their decisions.
  
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Technical Training is NOT Enough:
The Role of Critical Thinking, Problem Solving, Data Synthesis, & Presentation Skills 

There has been an increased emphasis and interest in STEM (Science, Technology, Engineering, & Mathematics) education to prepare the next generation for a new age of work. Colleges, particularly technical schools and community colleges, have invested heavily in career and technical education that seeks to broaden technical skills currently in demand (coding, software engineering, etc...). Ironically, though, being trained in the liberal arts and humanities may be more useful in the age of AI. In addition, there is data suggesting a surplus of STEM majors in some fields (see also this piece from The Atlantic). 

It won't be so much our technical skills that differentiate us in the 21st Century economy. Rather, being able to think critically, synthesize information, present arguments persuasively, and work effectively with others will be increasingly valued. 
Anyone working in the sciences knows that our knowledge and tools change fast. So, it may not be surprising to consider the skills we value now in coding, data manipulation, and analysis may not be what is needed 20 or even 10 years from now. 
"It is not only what you know but how you learn that will set you apart."
- Thomas Friedman, author of The World is Flat: A Brief History of the Twenty-first Century
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Graduate Students & Postdocs Have the Skills Needed for the 21st Century Economy 
Having the skills to quickly dissect a problem, synthesize and interpret data, and clearly present insights and recommendations from your work are incredibly valuable. Luckily, if you have been pursuing a graduate degree or postdoctoral work, you have experience in all these areas. Each day you are working to troubleshoot problems, synthesize your data with what is already known, and form coherent narratives around your findings. You are uniquely poised to make sense of the flood of data that is and will be produced and stored from the countless scientific publications, device interactions, and web searches that occur each day. 
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Even the hot career field of data science is focused heavily on communicating insights from analyses. This skill may become even more critical as computers takeover the work of data preparation and analysis, leaving the humans to extract insights from the work. 
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Ph.D.-trained scientists are uniquely poised to be the data interpreters we will need in the 21st Century. 
How to Hone & Leverage Your Transferable Skills Into Meaningful Work 
A changing world demands individuals who can change with it. Flexibility and the ability the learn and adapt to new technology will be crucial. Scientists possess these and other transferable skills but need to more effectively hone and communicate them to potential employers.
​In addition, current graduate students and postdocs must avoid becoming so focused in one domain or technique that they don't develop the transferable skills needed to succeed in the 21st Century economy. 
How can current trainees practice and perfect their transferable skills? 
Develop Teamwork & Leadership Skills
Taking leadership roles or volunteering in organizations that you are passionate about (including graduate student and postdoc associations) allows you to give back to your community while also honing your teamwork and leadership skills (for example, see). You can look to get involved with local or national organizations or local chapters of national organizations, potentially in research or career areas that interest you. 
​ 
Volunteering within an organization while continuing in your current role will also force you to improve your time and project management skills as you seek to fulfill your many obligations. In addition, working with others in these organizations, especially those from different backgrounds than yours, will teach you the importance of effective communication skills (listening, synthesizing, presenting), leadership, and consensus building to achieving success. 
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Check out Volunteer Match for community volunteering opportunities. ​
Communication Skills
You should also seek out opportunities to practice and demonstrate your communication skills. Join Toastmaters to practice your public presentation skills or volunteer to deliver a talk to a general audience (local science cafes/clubs are a great venue). You can also start posting articles on the web to demonstrate your written communication skills (LinkedIn has a great self-publication feature). Showing you can speak to a general audience and not just academics is crucial. Your future coworkers and customers will have a variety of backgrounds and speaking to them in an accessible way will increase the rapport you can build with them.  ​
Final Thoughts
While ​the future of work in the age of automation may seem scary, there is the real possibility that increased efficiencies will free us to do tasks that are mentally stimulating and personally rewarding. Instead of slaving over data we will be able to spend our time thinking about how it can be used to better the human condition. We will be able to spend more time interacting with one another instead of at our screens doing the menial tasks that often accompany knowledge-based work. 
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A more automated and efficient world will provide us more time to do what we enjoy and give back to others: to do meaningful work. By freeing us of routine, tedious tasks, AI also has the ability to unlock the vast untapped potential in so many of us...to allow us to pursue work and activities that are not motivated so much by their utility and profitability (as the machines will do much of that) as by their ability to bring happiness to our and others lives.
That is certainly a future worth aspiring towards.   
Further Reading
AI & Work

Where Machines Could Replace Humans - and where they can't (yet)

What jobs are affected by AI? Better-paid, better-educated workers face the most exposure

Is Technology About to Decimate White-Collar Work?

When Will AI Exceed Human Performance? Evidence from AI Experts

​
In the Age of AI (PBS Frontline documentary)

Human Flourishing in the AI Age - We Need a New Story


Education & the 21st Century Labor Market 
The Myth of the Science and Engineering Shortage
​

STEM Crisis of STEM Surplus? Yes & Yes

A Humanities Degree is Worth Much More Than You Realize

The Growing Importance of Social Skills in the Labor Market

You Can Do Anything: The Surprising Power of a "Useless" Liberal Arts Education (book)

The Future of Work
​Meaningful Work: Viktor Frankl's Legacy for the 21st Century (book)

Life's Great Question: Discover How You Contribute to the World (book)

The Globotics Upheaval: Globalization, Robotics, & the Future of Work (book)

MIT Initiative on the Digital Economy 
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Using Data Science and Artificial Intelligence to Improve Healthcare

9/30/2019

1 Comment

 
Data Analytics & Healthcare
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This article originally appeared on the Health:Further blog on October 14, 2018.
It has been updated with current news and findings.
Data science is a buzzy term not only in the technology sector but in the wider culture as well. It has seeped into the common vernacular and promises increased insights and knowledge extracted from the vast quantity of data being generated every day.

The use of data science in healthcare is growing and artificial intelligence (AI) represents a huge business opportunity in the space. However, the potentially identifiable nature of health records and ethical concerns about how the data should be utilized and by whom makes working in this space a challenge.

The recent publication of work suggesting AI may be as good as clinicians in diagnosing disease further highlights the increased importance this technology will have in 21st Century healthcare.

I spoke with data scientists from 3 different healthcare companies on how their groups are using data to improve the quality and efficiency of healthcare.
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axialHealthcare, Nashville, TN

Lindsey Clark, Ph.D., is Director of Data Science and Analytics at axialHealthcare in Nashville, TN. Since joining the company in 2015, Clark has watched axialHealthcare grow rapidly from 8 to more than 100 employees. Focusing on pain management and opioid care, axialHealthcare leverages medical, behavioral, and pharmacy claims data to drive improved patient care and financial savings for health insurers through technology-enabled capabilities.
In essence, axialHealthcare's goal is to understand which treatments are both effective and safe in the treatment of pain. Axial is also focused on determining if treatment approaches beyond potentially addictive opioids are viable for particular patients.

A big question at axialHealthcare is, “what does safe and effective pain management look like?” The answer seeks to ensure that 1) opioids are prescribed judiciously given their propensity for causing dependence and addiction and 2) that other pain reduction therapies are considered when warranted.
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Clark emphasized the unique challenges of working with health information, including the critical need for data security and privacy as well as navigating the complex United States health system. The focus for most of U.S. healthcare is on what is reimbursable by either health insurers or Medicare/Medicaid (federal & state payers). Thus, every company trying to improve cost efficiency in healthcare must think about how their recommendations fit into the payers’ current reimbursement framework.

axialHealthcare has organized its Research and Development (R&D) group into data science and statistics/communication branches that communicate closely but have different functions. These teams work with the product team whose job is to think about the value they can extract from data insights and models to benefit customers. The R&D groups also provide support to the company’s clinical outreach team comprised of licensed clinical pharmacists and engagement specialists who work to change provider behavior and improve patient outcomes, which ultimately reduces costs on the healthcare system and protects insurers from spending on ineffective treatments.
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axialHealthcare synthesizes a variety of data to improve patient care and drive savings for payers.
Most of the company’s data comes from insurance claims, but some is also gleaned from patient behavioral and electronic health record data. Although the team is always focused on new data-derived models of improved care and cost savings, it is critical for the data science team to align their projects with what the market needs.
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“Insurers, our main clients, are very focused on short-term costs and so it’s often critical that our company frame the work in a way that indicates both short-term and long-term costs can be improved through data insights. Selling clients on the long-term cost savings can be difficult, especially if the short-term effects are increased costs to insurers,” according to Clark.

This point illustrates the challenge of providing solutions that are good for the business of healthcare and also for the health of patients.

Framing information in a way where payers can see the long-term savings generated from costly approaches in the short-term is critical to enacting meaningful, effective interventions.
Ultimately, the company hopes to collect its own data for two reasons: 1) access to data can be a challenge and 2) variables that may be of interest to the data science team aren’t always available in the data collected by a third party. Nevertheless, axialHealthcare’s current approach has proven effective for patients and payers.

Artificial Intelligence (AI) in Healthcare

At the 2018 Health:Further Festival held in Nashville, TN, I talked with two companies working to use artificial intelligence (AI) to improve healthcare.

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Change Healthcare AI group, San Francisco, CA

Change Healthcare is the largest independent healthcare information technology company in the U.S., handling approximately 60% of medical claims. Alex Ermolaev is part of a growing AI team there. The goal of Change Healthcare’s AI group is to improve efficiency and add value on top of existing data management and analytics solutions.

See Alex's video on AI in Healthcare below.
Change Healthcare uses large, aggregated and de-identified claims, medical history, and treatment plan data from their databases to provide insights on how to increase the effectiveness of healthcare, particularly how to provide treatment that is more efficacious and economical. While Change has mostly claims data, Ermolaev said most of it is very extensive, often up to 400 pages per claim, so that meaning and insights can be extracted from the various doctor notes and other details from healthcare providers. Text from claims can be read and analyzed using natural language processing approaches to identify relevant information in the record.
Ermolaev, formerly at Nvidia, mentioned that most AI models can achieve very high accuracy (>95%) as long as the following 3 factors are available: 1) large amounts of data, 2) bigger/more complex models, 3) more computing power.

“The main limit to using AI in healthcare is the lack of large enough data sets,” according to Ermolaev. This shortage of data is not unexpected given the sensitivity of personal health information and the vast privacy protections in place. Thus, companies with access to the data have a great advantage when competing in the AI healthcare space.
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As AI and predictive analytics grow in healthcare, Ermolaev believes we are moving from evidence-based healthcare, where treatment decisions are based on what’s been proven effective for the population in general, to intelligence-based care where a particular patient’s medical history informs more personalized treatment.
I was surprised to hear Ermolaev mention that genetic information (frequently promoted in academic circles as the key to precision medicine) is often not required to develop personalized insights. This highlights the fact that currently available medical history, behavioral, and symptom data is often adequate in creating dramatically more effective personalized treatment plans.

UPDATE:
Change Healthcare announced its Claims Life Cycle AI in February 2019, which seeks to reduce the number of denials for medical insurance claims processed by healthcare providers (see infographic).
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Droice Labs, Brooklyn, NY

While at Health:Further, I also met with representatives from Droice Labs on Entrepreneur Alley at the 2018 Health:Further Festival, a showcase area where over 70 startup companies could meet with conference attendees.

Droice Labs brings the power of artificial intelligence to hospitals. The company’s technology provides personalized predictions of how a given treatment (e.g., a drug or a medical device) will perform for a given patient. This software solution is based on a combination of the latest medical research and learning algorithms, which together analyze how a treatment has performed on similar patients in the past by aggregating data from millions of patient records and treatment plans. This allows doctors to consider all of their options in real-time and choose the right treatment.
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Droice labs uses a variety of patient datapoints to better understand individual differences in response to medical treatment.
Droice Labs has approximately 30 employees and works with providers, payers, pharma, and government clients to build applications that augment processes and improve workflows. The goal of this work is to improve the quality of care for patients while also decreasing the burden on physicians with the ultimate outcome of increasing the efficiency of the healthcare system.

The company has been around for just over 2.5 years. The founders of Droice have extensive backgrounds in technology and AI and take a “deep dive” approach to their projects, trying to understand the causation behind their insights and results. They then communicate these findings transparently to their clients.
The relatively small company has a very collaborative culture with employees from a variety of backgrounds—tech, healthcare (including clinicians), scientific research—that bring different but complementary perspectives to their work. “The company is structured to be very horizontal, an organizational setup that fosters the sharing of ideas among all individuals in the team,” according to Droice Labs Co-founder & CEO Mayur Saxena, Ph.D.

Speaking to M. Saxena and Co-founder & Chief Product Officer Harshit Saxena (no relation), it is clear Droice Labs has a growth-focused, startup-like culture with a hunger from employees to continue to innovate and do more. The company has a mission that appeals to young workers that want to work for a values-driven company. “One measure of success at Droice Labs is how many people we were able to impact by our work today,” says M. Saxena. “Do the insights we develop increase the well-being of an extra 1,000 people? How can we improve things to increase that impact?”
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Both of the founders agree with a point mentioned on the main kickoff stage at the Health:Further Festival: healthcare touches everyone. When asked what got them into using their analytic skills in the healthcare space instead of a traditional technology company, M. Saxena emphasized the common human experience of healthcare: “my thought was, if I am going to consume it, I might as well work to improve it.” He went on to say that healthcare is full of great people that work tirelessly to improve human life and that the industry needs technology to enable healthcare workers to do their jobs more easily and effectively.

At companies like Droice Labs, Change Healthcare, and axialHealthcare, the approaches may differ but the goal is the same: to improve healthcare in the 21st Century through data and insights.

Read More
Artificial intelligence in healthcare: Past, present, and future
Artificial intelligence in healthcare
Applied data science in patient-centric healthcare


Additional Resources

Want to get into data science?
The Insight Data Science Fellowship program offers a fabulous training opportunity with demonstrated success in job placement afterwards.

NC State's Institute for Advanced Analytics offers a well-respected Master of Science in Analytics degree with excellent career outcomes for graduates.
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    A neuroscientist by training, I now work to improve the career readiness of graduate students and postdoctoral scholars.

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