🦾How You Can Leverage:

What happens when AI actually works?

No, we're not talking about ChatGPT spitting out a half-decent blog post or DALL-E making a funky image of a cat riding a unicorn in a cowboy hat. 

(Yeeee-meawwwwwww!) 

We're talking about when AI fundamentally changes how we work, learn, and live.

• When it boosts productivity by 50%. 

• When it predicts heart disease 15 months before symptoms appear. 

• When it levels the playing field in education and finance for underserved communities.

• Sounds amazing, right?

But here's the rub:

What happens to the millions of jobs that get displaced along the way? 

Or when change happens faster than companies can keep up with it? 

How do we ensure AI's benefits reach everyone, not just the privileged few?

These are the thorny questions that Dia Wynn’s gotta tackle. 

As the Responsible AI Lead at Amazon Web Services, she and her team are at the forefront of figuring out how to harness AI's immense potential while mitigating its risks.

On today's show, Dia's dropping truth bombs left and right. She's dishing on why responsible AI isn't just for tech geeks, how companies can teach old dogs new AI tricks, and why your skills might have a shorter shelf life than that milk in your fridge.

But it's not all doom and gloom. Dia's got a vision of AI creating a more equitable world - if we play our cards right.

Let’s shuffle and deal em — here’s the takeaways from today’s show you need to know. 

👇

Back in the pre-Generative AI days, you could hang your hat on that single skillset and ride that horse for decades. 

Not anymore. 

The half-life of skills is shrinking faster than ever, Diya pointed out. 

(Now, AI has shrunk some half-life of skills down to less than 2 years!) 

Gone are the days when a degree set you up for life. Now, it's all about constant learning and adaptation.

Try this: 

Diya was quick to point out that the nature of work is changing. As LLMs become more and more capable, some our tried-and-true skillsets unfortunately become less and less meaningful. 

Truth hurts, we know. 

Diya gave the example of video production. A few years ago, it was tough, expensive and not available to all. 

Now AI can legit just edit and create videos with the push of a button. 

Here’s our best tip — AI is raising the skill floor SO high that it’s getting near the ceiling. 

Instead of deepening one skillset, try widening your range of skillsets, powered by AI. 

…. If we get it right. 

Yeah, the whole ‘get it right’ part is dicey, we know. 

Responsible AI isn't just about building ethical algorithms. It's about considering the entire ecosystem – from development to deployment to real-world impact.

Diya told us that the responsibility of AI doesn’t lie solely with tech companies, or people with her job title. 

It’s for all of us. 

it’s a societal challenge requiring cross-sector collaboration. Policies, business strategies, governments and community efforts must align to create a balanced AI ecosystem.

Yeah, we think AI is all about productivity and efficiencies. But what about doing good and leveling the playing field for marginalized communities?

(Good things can happen WHEN AI works.)

Try this:

It might be habit to think of job loss of the ramifications of ‘when AI happens.’

But it can level the playing field in education, healthcare and society.

Diya highlighted an AWS partnership with Cerner where their AI powered solution helped predict congestive heart failure up to 15 months before clinical manifestation by querying anonymized patient data to uncover potentially life-saving insights.

Here’s some other quick examples:

• Education: AI tutors like Carnegie Learning’s Mika provide real-time, personalized math help, improving learning outcomes for all students​

• Healthcare: NHS uses AI-driven HeartFlow Analysis to reduce gender bias in heart disease diagnosis

• Healthcare: AI-based liquid biopsies detect cancer faster, enabling earlier treatment​

• Healthcare: AI boosts radiology accuracy by 44%, reducing missed diagnoses in conditions like lung cancer

Stop skipping over this part. 

(We’re looking at your, business owners and decision-makers.) 

Cuz here’s the thing — AI implementation changes things for the bad and the good. 

Like we talked about on today’s show, there’s potential utopian outcomes and dystopian ones.

You have to be prepared for both when it comes to your business.  

Try this: 

Ask this simple question: 

What happens if AI implementation in your biz means that 50% of your employees….. no longer have any tasks or projects. 

(Thanks a lot AI!) 

That can be a good thing or a bad thing. 

Number crunchers will say cut those jobs and hand em over to AI. (Many are going this route.) 

The Responsible AI leaders among us will have a plan in place WELL BEFORE AI implementation goes from a tip-toe to a sprint. 

• Will you create new lines of business? 

• New departments? 

• New regions? 

• More services? 

And also, you’ve gotta be ready to upskill and reskill your people. 

If only there were some sort of blueprint on upskilling and reskilling….. 

Oh wait! Diya shared that: 

AWS is tackling this head-on with a multi-pronged approach:

• Company-wide AI awareness training (think annual compliance, but for AI)

• Role-specific AI education (because what a data scientist needs to know is different from what a product manager needs)

• Public access to AI learning resources through their Skill Builder platform

Refer Friends to Win a Year of ChatGPT!

(Or your fave LLM like Claude, Gemini, Copilot, etc)