How will the ability and wonders of Artificial Intelligence (AI) take us to completely new levels of civilization in the next 20 years?
This technology has already been implemented into our lives in small ways, but it’s about to get even more interesting. Here’s a simple explanation of how this technology actually works, and how it has and will be developed to make our lives safer, create more jobs, and add more substance to our experiences.
[Preface from Dr. Lynda]
Today on Ever Widening Circles (EWC), we begin a journey to understand the possibilities that emerging technologies will bring to all our lives. Our aim is to help us all understand the remarkable innovations that we hear about, like AI, Bitcoin, Blockchain, and the Internet of Things. I suspect these leaps will change everything, so we can reduce our fears about them if we get familiar with the territory and maybe even celebrate what will be an incredible ride!
First, for such an expansive, complex trip, we’ll need a great tour guide: someone on the inside who can translate tech-speak to a language we can all use around the dinner table. True to EWC standards they will have to have credentials we can trust, and they’ll have to be someone we’d all love to have a cup of coffee with.
As luck would have it, we’ve found such a person in a new friend to EWC, Guilherme Araújo! He contacted us recently, thrilled to have discovered Ever Widening Circles, and has offered to be a frequent guest writer and guide to the edges of our tech landscape. He has a long resume in technology and has recently led the Cyber Security and Watson divisions at IBM Brazil. His passion for communication and connection is just what we needed for the journey.
(And if you work in the tech industry, this article may be a perfect way to help your friends and family understand the meaning and possibilities your work is advancing!)
Without further adieu, here’s Guilherme…
Did you know that you and I have 100 times more computational power in our mobile phones than the computers used to put a man on the moon in the 1960’s?
As amazing as that seems, an observation called Moore’s Law could have almost predicted it!
Yes, in 1965, a brilliant man named Gordon Moore proposed that there would be a doubling of computational power every two years, and 50 years of progress has led us to the point where the reach of technology can be a bit confusing and even alarming until it is better understood.
That’s why I’m offering to write some articles for EWC: to help us understand the amazing times we are living in. Artificial Intelligence (AI) is a great place to start.
We’re hearing a lot about AI lately, but it’s actually not new at all. In fact, the initial research was born in a workshop at Dartmouth College in 1956 1, and a few years later, Arthur Samuel provided us with the definition for another term we will hear a lot about in the coming years: Machine Learning: “giving computers the ability to learn without being explicitly programmed”. 2
So why did it take so long for AI and Machine Learning to become useful? The response is two-pronged and very simple: computational power and connectivity. Sixty years ago, computers were not connected over distances, personal computers did not exist, and mathematical tasks were done by gigantic “mainframe computers”, filling entire rooms.
Here’s a clever video to put that into perspective and get us up to speed on the history of AI.
Why is AI such a huge sea change?
So, now let’s take a moment to understand the machine learning behind Artificial Intelligence. To make it easier, let’s compare it to the traditional programming processes we’ve become so familiar with.
With traditional programming, software developers have to insert a rule for each command in order to get the program to perform the expected task. As you might imagine, the complexities are exponential, because you have to build contingencies by hand for every rule which amounts to a complex system of rules that must be constantly be updated as users need more and more from capabilities or as issues – “bugs” – have to be fixed.
Machine learning, on the other hand, is not built by rules, but with data, and specifically repetitive data.
So if I want to train a machine to execute a task, I will use a methodology based on repetitive examples of the same thing. (There are other ways that machine learning is accomplished but we could address those in another article.)
And there’s another important variable: Storage Capacity. When you pick up your cell phone, you have in your hands 51,000 times more capacity than when we put a man on the moon in the 1960’s. Now with our incredible processing capacity and storage, machine learning systems can look at available data and determine what those examples mean.
This doesn’t happen alone – humans have to feed Artificial Intelligence the data and make adjustments by feedback – like training a student to score well on a test at school. The more data you give a machine learning system, the better and smarter the application will be.
Bottom line: Traditional programming is becoming obsolete because, in order to keep up with machine learning, it would need millions of constantly updated rules to even come close to getting the same results.
I know that the public has some fears about AI, but it may be a great partnership! Think about it this way: Artificial Intelligence will augment human knowledge, allowing professionals from all areas to increase their raw productivity, freeing up time to focus on creative activities that could solve some of the world’s greatest challenges.
It could be a win/win: as we escalate our capacity to discover new things, we can create ways to make the world a better place to live for everyone.
Will AI cause the loss of jobs?
We don’t have to look very far to see the answer. The workforce has been changing due to technology for at least 40 years! What is already happening is that dull, dirty, and dangerous jobs are disappearing and being replaced by better jobs that are more rewarding and much safer.
Yes, workers in fading industries will have to seek retraining, but people are remarkably resilient and resourceful. Many are embracing these changes. As long as that trend continues, this particular fear may largely disappear.
Need some hard numbers? Gartner (an independent technology research) made an estimation that in 2017, there were 1 million new job positions created to work with emerging AI technologies, and only 300 thousand people already capable to work in those jobs. In 2020, they project a loss of 1.8 million jobs but a gain of 2.3 million, making it a net gain of 500,000 jobs. 4
It would seem sensible to take that assessment to heart, and focus on creating training systems that would ready that workforce: lifting people up across cultures and education levels.
Again, we can look at the past for examples of what is possible when a leap in technology offers a leap to society. Sebastian Thrun, founder of Udacity (a for-profit educational organization with all the latest technology classes) stated the following:
“In the beginning of the agriculture revolution when the steaming engine was invented – it made farmers 100 times more efficient and transformed where populations would work. 90% of Europeans were working on farms 100 years ago – nowadays – it is less than 2%. Artificial Intelligence will take repetitive office jobs and make it 100 times more efficient.” 5
It might be helpful for us all to remember that with every leap in civilization — from the invention of the wheel and the printing press, to the discovery of that steam engine — there have always been trade-offs and challenges to get it right for the sake of our shared future.
Here’s a great, short video that makes the point beautifully that as jobs are lost, better more rewarding jobs will follow:
How does AI already serve us?
Most of us interface directly with AI when we interact with a company’s customer service strategies. Customer experience is a huge area of based data applied to machine learning. Machine Learning allows for computers to speculate about who we are and what we want, and they are giving us even more personalized experiences. If you’ve ever interacted with a “chatbot” in a customer service interaction, then you’ve taken the first, tiny step into the world of AI.
Of course, past that level of interaction comes the many ways AI can help us execute tasks faster and more accurately in our jobs. Cognitive assistants can help a lawyer, an accountant, a teacher, or a doctor!
Here are just a handful of fascinating and inspiring examples of how AI is making a measurable difference for society:
Want a cure for cancer?
Based on more than 30 million research papers that were curated by the best oncologists and scientists, a machine learning system can be trained to comprehend that vast body of literature on cancer to arrive at insights unavailable to us now. (It would be impossible for any human to be able to read, comprehend, and infer new models.)
Doctors will soon be able to input data from their patients and get a second opinion based on this vast ocean of data. This will help oncologists be more precise, and who knows, help them find new ways to combat cancer.
Here’s a short news report that points to possibility:
What about ending hunger in the world?
One of the biggest challenges facing humanity is how to feed the world that has been increasing in population. Due to non-sustainable practices, we have less fertile soil and unpredictable weather conditions that endanger our ability to produce food, to feed everybody properly. Well, Artificial Intelligence is already helping agribusiness to increase its productivity and get more from the same amount of land.
And it’s not just the “big guys” who are finding help in this trending technology! Here’s a great example of how this will change lives for farmers all over the world:
What about social inclusion?
Using AI to create ever more personalized experiences will be one of the most challenging and inspired uses for AI. Imagine the unique experiences one could have in a museum with AI personalizing the visit. What if our education system could evolve to elevate all the unique gifts a particular child has?
Sounds like science fiction, hm? Check out what was done at Pinacoteca in Sao Paulo. Artificial Intelligence was trained by art specialists to have a conversation with visitors on 7 relevant works of art, being able to answer technical questions to contextualization ones. This was inaugurated in April 2017 with the capability of answering around 7,000 questions from visitors. After 8 months, it is now capable of answering more than 400,000 questions.
This is machine learning at its best! Based on what the public has asked, specialists have to continuously train the system, helping it learn alongside everybody’s experiences. Take a look…
Amazing, don’t you think?
It is difficult to set a high bar on the possibilities with this technology, and I am inspired.
Are we about to see an evolution in humankind? Are we about to experience a transformation that will fill the most optimistic dreamers with wonder?
I look forward to finding out!
” I can’t change the direction of the wind, but I can adjust my sails to always reach my destination” -Jimmy Dean
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- “The Dartmouth Artificial Intelligence Conference: The Next Fifty Years.” The Dartmouth Artificial Intelligence Conference: The next 50 Years. Dartmouth, n.d. Web. 18 Jan. 2018. <https://www.dartmouth.edu/~ai50/homepage.html>. ↩
- Al-Darwish, Maryam. Cmu.edu. Machine Learning, n.d. Web. 12 Jan. 2018. <http://www.contrib.andrew.cmu.edu/~mndarwis/index.html>. ↩
- “A Brief History of Artificial Intelligence.” YouTube. BootstrapLabs, 13 Sept. 2017. Web. 10 Jan. 2018. <https://www.youtube.com/watch?v=056v4OxKwlI>. ↩
- Hiner, Jason. “AI Will Eliminate 1.8M Jobs but Create 2.3M by 2020, Claims Gartner.” TechRepublic. Tech Republic, 02 Oct. 2017. Web. 12 Jan. 2018. <https://www.techrepublic.com/article/ai-will-eliminate-1-8m-jobs-but-create-2-3m-by-2020-claims-gartner/>. ↩
- Udacity. “Keynote: Sebastian Thrun | Udacity Intersect 2017.” YouTube. Udacity, 31 Mar. 2017. Web. 12 Jan. 2018. <https://www.youtube.com/watch?v=1HiEFxmOTGo>. ↩
- “How Smart Is Today’s Artificial Intelligence?” YouTube. Vox, 19 Dec. 2017. Web. 10 Jan. 2018. <https://www.youtube.com/watch?v=IJKjMIU55pE&feature=youtu.be>. ↩
- “IBM’s Artificial Intelligence System Joins Cancer Fight.” YouTube. CBS This Morning, 07 Oct. 2016. Web. 15 Jan. 2018. <https://www.youtube.com/watch?v=PXf8Nq_zx0A>. ↩
- “The Farming Robots of Tomorrow Are Here Today | The Future IRL.” YouTube. Engadget, 15 Aug. 2017. Web. 10 Jan. 2018. <https://www.youtube.com/watch?v=Rl77FVobxVI>. ↩
- “With Big Data, IBM Helps Create A Sustainable Food Supply.” YouTube. IBMSocialMedia, 23 Apr. 2014. Web. 11 Jan. 2018. <https://www.youtube.com/watch?v=bu1wC-hCuC0&feature=youtu.be> ↩
- Baragona, Steve. “Small Farm, Meet Big Data.” VOA. VOA, 27 June 2017. Web. 10 Jan. 2018. <https://www.voanews.com/a/agriculture-technology-small-farms-big-data/3918239.html>. ↩
- “The Voice of Art | With Watson.” YouTube. IBMbrasil, 13 Apr. 2017. Web. 10 Jan. 2018. <https://www.youtube.com/watch?v=m9jT6nStyCQ&t=11s>. ↩