World Changing: Data Science and AI | Fred Blackburn | TEDxWakeForestU

Summary

The speaker discusses the transformative impact of data science and AI on human history, drawing parallels with the Industrial Revolution. They highlight how AI is evolving, with machines now performing tasks previously done by humans and creating new job opportunities. The talk explores the differences in learning between humans and machines, the rapid growth of data, and recent advancements like AI creating art and potential cures for diseases. The speaker also envisions a future where AI and humans collaborate to solve complex problems, mentioning projects like 'super agent' for law enforcement and disaster response innovations. The talk concludes with a call to action for the audience to prepare for a future where AI plays a significant role in enhancing human capabilities.

Takeaways

• 🌟 The speaker is excited to discuss how data science and AI are on the verge of causing significant changes in human history.
• 🏭 The Industrial Revolution led to machines performing manual tasks, creating new jobs and improving quality of life, despite initial fears of unemployment.
• 💻 The computer revolution allowed machines to perform low-level cognitive functions, freeing humans to tackle more complex problems.
• 📈 The amount of data created in the last two years accounts for 98% of all data in the world, highlighting the rapid growth of knowledge.
• 🤖 Machines learn differently from humans, using simulations, anomaly detection, and attempts to emulate the human brain.
• 🚫 Despite advancements, machines still struggle with simple tasks like distinguishing between certain images, showcasing the complexity of the human brain.
• 💊 AI has recently been able to create new cures and vaccines independently, such as a promising malaria drug, which is a significant leap from its past capabilities.
• 🎨 Machines are now capable of creating art that is visually appealing to humans, a field where they previously struggled.
• 🚗 Driverless vehicles are a reality, but they still face limitations such as operating in adverse weather conditions.
• 🤝 The future will involve humans working alongside machines, with AI taking on repetitive tasks and allowing humans to focus on more complex challenges.
• 🔮 The speaker envisions a future where AI can help solve previously insurmountable problems, improve life expectancies, and make the world a better place.

Q & A

What major historical change is the speaker comparing the current AI and data science revolution to?

The speaker compares the current AI and data science revolution to the Industrial Revolution, highlighting how machines began to perform manual functions previously done by humans.

How did the Industrial Revolution impact job displacement and the creation of new jobs?

The Industrial Revolution initially caused concern about job displacement, but it ultimately led to an explosion of new jobs that didn't exist before, and people moved from farms to cities, experiencing an increase in quality of life and life expectancy.

What is the speaker's perspective on the impact of the computer revolution on human work?

The speaker believes that the computer revolution has freed people from low-level cognitive functions, allowing them to take on bigger and more challenging problems.

What percentage of the world's data was created in the last two years according to the speaker?

The speaker states that 98% of the data in the world today was created in the last two years.

How does the speaker describe the difference between how humans and machines learn?

Humans learn through evolution, experiences, and culture, while machines learn through simulations, anomaly detection, and by trying to emulate the human brain.

What is the paradox the speaker mentions regarding machines and simple tasks?

The paradox is that while machines can perform complex tasks, they struggle with simple tasks that humans find easy, such as distinguishing between images of dogs and blueberry muffins.

What recent advancement has allowed machines to create art that is visually appealing to humans?

The speaker mentions that machines have recently advanced to the point where they can create art that is visually appealing to humans.

What is the 'super agent' concept the speaker is excited about?

The 'super agent' concept is about equipping law enforcement officials with data through sensors and biometric scans to enhance their capabilities, similar to Robocop but without the negative aspects.

How does the speaker suggest improving disaster response with technology?

The speaker suggests leveraging cryptocurrencies and the digital economy, like Uber and Airbnb, to make disaster response more effective and efficient.

What is the 'Data Science Bowl' and what was its achievement last year?

The 'Data Science Bowl' is a competition for social good, and last year it created algorithms that allowed radiological technicians to scan X-rays more effectively, particularly for early detection of lung cancer.

What four things can we be confident about the future of machines according to the speaker?

The speaker is confident that machines will perform more repetitive and higher-order cognitive tasks, serve as co-workers, interface with humans in a more natural way like other humans, and help solve unprecedented problems more effectively.

Outlines

00:00

🌟 The Impact of AI and Data Science on Human History

The speaker expresses excitement about discussing the transformative potential of data science and artificial intelligence (AI). They draw parallels between the current technological revolution and historical milestones like the Industrial Revolution, which initially caused concern over job displacement but ultimately led to increased quality of life and new job creation. The speaker emphasizes the rapid pace of data creation and knowledge expansion, noting that 98% of the world's data was generated in the last two years. They also highlight the differences in learning between humans and machines, with the latter improving through simulations, anomaly detection, and attempts to emulate the human brain. Despite machines' capabilities in certain areas, they still struggle with tasks that are simple for humans, such as distinguishing between complex images. The speaker concludes by suggesting that the combination of human curiosity and powerful machines will lead to significant advancements.

05:02

🚀 Advances and Limitations of AI in Various Fields

The speaker discusses the advancements in AI, noting that machines are now capable of tasks that were unimaginable six months ago, such as creating cures for diseases and producing visually appealing art. They introduce 'Eve', a machine that has contributed to the development of a promising malaria drug. The speaker also mentions the increasing role of AI in personalized medicine, disaster response, and strategy games. However, they acknowledge the limitations of AI in areas like language comprehension and creative writing. The speaker is excited about the future of AI, particularly in the development of 'super agents' that can assist law enforcement with data and biometric scanning, and in improving disaster response through digital economy tools. They also mention the Data Science Bowl, which focuses on using AI for social good, such as detecting lung cancer more effectively.

10:03

🔮 Envisioning the Future with AI

The speaker encourages the audience to think about the future and how AI will transform various fields. They predict that machines will take on more repetitive and higher-order cognitive tasks, freeing up human intellect to tackle more complex problems. The speaker anticipates a future where humans will work alongside machines as co-workers and interact with technology more naturally, similar to human interactions. They express optimism about the potential for AI to solve previously insurmountable problems, improve life expectancies, and address global challenges more effectively. The speaker advises the audience to consider how they can prepare for these changes and the skills they will need to succeed in a future where AI plays a significant role. They recommend a book by colleagues for further reading on AI and its future implications.

Keywords

💡Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data. In the video, data science is highlighted as a key driver of innovation, combined with artificial intelligence, to bring about significant changes in human history. The speaker emphasizes the importance of data science in creating new skillsets and capabilities, such as the development of new drugs and the enhancement of law enforcement capabilities.

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video discusses AI's role in performing tasks that were previously done by humans, such as cognitive functions, and how it is on the verge of causing unprecedented change. The speaker also touches on the paradox of AI, where machines can perform complex tasks but struggle with simple human-like interactions.

💡Industrial Revolution

The Industrial Revolution is a period during the 18th and 19th centuries where agrarian, rural societies in Europe and America became industrial and urban. In the context of the video, the Industrial Revolution is used as a historical reference point to compare the scale of change that data science and AI are expected to bring. The speaker mentions the initial concerns about job displacement during the Industrial Revolution, drawing parallels to current debates about AI and job security.

💡Machine Learning

Machine learning is a subset of AI that provides systems the ability to learn and improve from experience without being explicitly programmed. The video script discusses how machines learn differently from humans, through simulations, anomaly detection, and by attempting to model the human brain. Machine learning is central to the advancements discussed, such as the creation of new drugs and the improvement of disaster response.

💡Cognitive Functions

Cognitive functions refer to the mental actions or processes through which an organism perceives, thinks, and gains knowledge and understanding. In the video, the speaker contrasts the manual functions of the Industrial Revolution with the cognitive functions that machines now perform, such as problem-solving and decision-making, which have been traditionally human domains.

💡Digital Assistants

Digital assistants are AI-powered software programs that can perform tasks like answering questions, setting reminders, and providing information. The video mentions Siri and Alexa as examples of digital assistants that can perform simple cognitive functions but struggle with more complex interactions, illustrating the current limitations of AI in mimicking human conversation.

💡Driverless Vehicles

Driverless vehicles, also known as autonomous vehicles, are self-driving cars or trucks that use sensors, cameras, and AI to navigate without a human driver. The video script points out the limitations of current driverless technology, such as the inability to operate effectively in adverse weather conditions like snowstorms, indicating that there is still much work to be done in this field.

💡Repetitive Tasks

Repetitive tasks are those that involve the same actions performed over and over again, often without requiring much cognitive thought. The speaker in the video suggests that machines will increasingly take over repetitive tasks, freeing up humans to focus on more complex and challenging problems, thus changing the nature of work and the skills required for various jobs.

💡Human Curiosity

Human curiosity refers to the innate desire to learn, explore, and understand the world around us. In the video, the speaker emphasizes the importance of combining the power of AI with human curiosity to drive innovation and solve complex problems. This concept is central to the idea of creating a 'super agent' that can assist law enforcement by leveraging data and AI.

💡Disaster Response

Disaster response involves the actions taken to prepare for and address the immediate effects of a natural or man-made disaster. The video discusses how the use of AI and digital technologies can revolutionize disaster response by making it more efficient and effective. The speaker gives an example of working with FEMA to leverage cryptocurrencies and digital economy platforms like Uber and Airbnb to improve disaster preparedness and response.

Highlights

The speaker expresses excitement about the potential of data science and AI to revolutionize human history.

Comparison of the current AI revolution to the Industrial Revolution, noting the creation of new jobs and improved quality of life.

The rapid increase in data creation, with 98% of current data existing in just the last two years.

Machines are built to learn differently from humans, through simulations and anomaly detection.

Machines' inability to perform simple tasks like distinguishing between dogs and blueberry muffins.

Advancements in AI that now allow machines to create art and develop new cures for diseases.

The paradox of AI excelling in complex tasks but struggling with simple human-like inquisitiveness.

The potential for AI to take over repetitive tasks, freeing human intellect for more challenging problems.

The concept of 'super agents' that could enhance law enforcement capabilities with data and biometric scanning.

The use of AI in disaster response, aiming to improve efficiency and effectiveness through digital economy tools.

The Data Science Bowl, a competition that developed algorithms to improve medical imaging analysis.

Predictions for the future where machines will perform more cognitive tasks, and humans will work alongside AI.

The opportunity for AI to address previously unsolvable problems and improve life expectancies significantly.

Advice for the audience to consider how AI will change their future careers and the skills they will need.

Recommendation of the book 'The Mathematical Corporation' for further understanding of AI's trajectory.

Transcripts

thanks Zoey for that really warm

introduction I can't tell you how

excited I am to be here today with you

all talking about a topic that I'm

really passionate about and that's how

data science combined with artificial

intelligence has us on the precipice of

unprecedented change of the course of

human history and if you think real

quick if you look back over some really

major changes that have occurred in

human history of course you've had the

Industrial Revolution the Industrial

Revolution as you know was the first

time which machines began to perform the

functions that humans previously did in

this case manual functions and at the

time there's a lot of concern as to how

humans have we put out of jobs you know

the rapid unemployment but none of that

happened when the Industrial Revolution

unfolded of course there's some

displacement but what we saw was a huge

explosion in new jobs that didn't exist

before we saw people moving from farms

into cities we saw a huge increase in

the quality of life in life expectancy

and that unfolded over about a 90 year

period and then fast forward to about

1950 with a start of the computer

revolution and now we have machines not

performing manual functions that humans

used to perform but actually now

providing low-level cognitive functions

that which freed up people to take on

bigger more challenging problems than

than they had in the past now fast

forward to today we have the computer

revolution we have the access to

information anytime anywhere it's at our

fingertips and we have this explosion of

new skillsets like data science this is

all coming together to create a scenario

where we're about to take significant

leaps forward in what we're capable of

as humankind so it's really I think

important to think about the differences

between how humans learn and how

machines learn before we get into what

the future could very much look like

humans basically learn through evolution

experiences in culture and over the

course of human history there was a

pretty pretty steady trajectory going

forward then with the advent of

computers all of a sudden you saw that

he

knowledge increasing at phenomenal rates

most people don't realize but 98% of the

data that exists in the world today was

created in the last two years that's a

stunning factor so you just think about

what that means for our knowledge of the

world around us

now machines on the other hand we built

machines to learn a little bit

differently than humans we built them to

learn through simulations through

anomaly detection and by trying to model

the human brain and trying to emulate

the human break we're not really far

along in a slaughter point but that will

be something that will certainly unfold

in the years to come so machines can do

some amazing things but there's also a

paradox here there's some simple things

that they can't do so as you know you

can interact with technology now you can

interact with Siri you can interact with

Alexa and Syria or Alexa will perform

simple cognitive skills or functions for

you but you really can't have a

conversation with Alexa if you've ever

tried having a conversation with any of

the digital assistants it's really

really frustrating there's a lot of work

to be done there everybody's heard about

driverless vehicles we that's certainly

the future but there's still great

limitations that exist that machines

have yet to be able to overcome

for instance you can't operate

driverless vehicles in a snowstorm in

fact you can't really operate it in a

heavy rainstorm so there's a lot of work

that needs to be done there as well now

there's a couple things on this list

here that when we built this these slide

six months ago machines couldn't do know

the ones that are crossed out six months

ago machines weren't able to create new

cures for diseases or vaccines for

diseases and I'll talk about that in a

moment

and although machines are able to create

art they weren't able to create art that

was visually appealing to humans now we

can so again we're coming of a very very

long way the other thing that machines

can do is they can really whip humans in

anything that's a mathematical base or

strategy based game so those are things

that you know we're going to rely on

future on machines for in the future but

what machines can't do is they can't be

inquisitive they can't take lessons

learned in one field and apply them to

another and that's where humans come in

and it's very powerful when you think

about the marriage

of these increasingly powerful machine

with heat with the human curiosity so

again to the paradox between what what

machines could do in what humans can do

a three-year-old can ace the test here

and pick out which one are the dogs in

which one of the blueberry muffins

computers are terrible at this they

don't even get it right 50% of the time

which really goes to the complexity of

the human brain and what we're capable

of and what machines are capable now it

doesn't always have to be about food in

animals but another picture here -

between doughnuts

I'm sorry bagels and dogs computers

can't tell the difference yet everyone

sitting in this room could easily tell

that there's huge differences here so as

Mark Zuckerberg said we've never been

closer or further away from the future

of AI now in terms of some of the really

awesome things the machines could do

normally we want to show this slide to

folks I asked folks to say okay can you

pick out Eve in this picture kind of

like Where's Waldo and everybody picks

the scientist peering through the

microscope on the right there that's not

Eve

that's a scientist who is working with

EE - Eve is the machine that you see

here Eve is a really remarkable machine

as I mentioned before 6 months ago a

year ago we did not have machines coming

up with cures independently of humans

now we have a very very promising

malaria drug and you think about the

years that have been spent trying to

come up with a vaccine for malaria and

here you have Eve doing it in the course

of six months to a year and that's just

the start

tremendous possibilities there other

things that machines can do better than

humans were at the point now with you

know based on your genetic code machines

can analyze and determine the best

treatments for diseases in afflictions

that you may have way better than then

doctors can in many cases as I mentioned

before we have driverless vehicles we

have driverless not just cars and trucks

but we've driverless drones airplanes

submarine ships and a lot of these

repetitive tasks again will be done by

machines in the future so limitations

but we are making lots of progress I

mentioned the example of

about art and although machines are

producing art that's visually appealing

we machines really do struggle still

with a written word it's got something

to do with the complexity of language

and so machines aren't able to write

poems that are appealing to humans or

write songs or write books or novels but

maybe someday the other thing that

machines I Ben should just really whip

humans at is any game of strategy

you know the game go here or chess or

any type of complex strategy game we

just can't beat machines anymore

now I'm also really excited about where

we're going in the future so Booz Allen

as you as Zoe mentioned we're consulting

in technology firm been around for about

a hundred years and we've really pivoted

it of last four or five years to really

focus on this combination of Technology

and human curiosity and what that can

create to make the world a better place

and one of the things I'm super excited

about is we have a concept that we're

leading up called super agent and it's

the idea is that you're going to help

law enforcement officials be infinitely

more capable than they are today and

it's not about arming and with more

weapons it's about arming them with data

and so what the super agent concept is

think about Robocop without all the

negative aspects of it it's a series of

sensors that a law enforcement official

wears as a inter-row and it

automatically scans that room in

determines if there's guns knives or

anything threatening that that officer

should be aware of it's also scans the

room for all the individuals that are in

it and does a biometric scan to see if

any of those are people of interest or

people who have dangerous backgrounds

and the last thing I'm really excited

about on this super aging concept is

we're going heavy into this concept of

micro-expressions and if you've ever

studied the field of micro-expressions

it's fascinating really aberrant

behavior is often characterized by very

strange combinations of micro

expressions so our theory is is that if

we can identify what those micro

expressions are that causes somebody to

flee order to causes somebody to act in

a violent way we could certainly make

law enforcement much much more effective

then than they are today the second area

that I should highlight that's really

cool we're working with FEMA the Federal

Emergency Management Agency to really

change the way we respond to disasters

disaster response today is inherently

inefficient ineffective we often spend

40 10 20 times what it would cost in a

non disaster type environment and we're

trying to leverage crypto currencies

we're trying to leverage the digital

economy like uber and airbnb to make the

process of responding in preparing for

disasters way more effective in

efficient that's something we could have

never done even three or four years you

know the last area I'd highlight that

were excited about we launched if you've

ever heard about it the data Science

Bowl it's basically data science for

social good and we were able to create

last year a set of the algorithms that

allow ready

radiological technicians to scan x-rays

more effectively than humans we focused

on lung cancer and could you detect lung

cancer quicker in earlier with humans

with machines and you can't humans and

I'm very proud to say that you can and

so we're excited to see where that goes

and other types of imagery related

medical applications and so yeah as we

go forward in the future there's four

things you can absolutely be confident

of machines will be doing more and more

repetitive higher order cognitive tasks

we're freeing up the human intellect to

take on more and more challenging

problems you will have machines as

co-workers which is going to be weird

but you will and in addition to having

machines s co-workers

you're not going to interface with

technology like we do today where you

sit down in front of a keyboard you type

stuff in you're going to interface with

technology like you do with other human

beings and I think that's exciting but

really when you get right down to what's

the most exciting part about this is

that unprecedented change we have the

ability in front of us to fix problems

that we never dreamed of fixing before

we have the ability to create increased

life expectancies by orders of magnitude

to clean up the oceans to clean up the

environment

and really do some amazing things and

doing it cheaper faster and better than

we ever did before so my advice to you

unlike the first speaker it's been a

little bit longer for me since I was

sitting in your shoes but I do vaguely

remember those those years think about

the future imagine 10 years from now

imagine your field of study imagine

you're drinking job you're working your

dream job

think about those repetitive cognitive

functions that you won't have to do

anymore

and instead what will you focus your

time on what kind of bigger problems can

you can you take on that you wouldn't

have been able to do if it wasn't for

the advent of artificial intelligence

and also think about what other skills

you need to be able to take on those

those projects and I think if you do

that you can have a really really

successful future now if you want to

learn more about AI and where it's

headed to my colleagues just Sullivan

and Angelucci tavern wrote this book the

mathematical corporation brought several

copies by and to have it in the library

encourage you to check it out or drop me

I know I'll I'll make sure we get that

post so don't make sure we get you guys

copy the book thank you so much

[Applause]