A Few Practical Thoughts on Microsoft's Future of Work 2023 Report
And some image prompt experiments from the weekend!
I hope you had a great and restful weekend! If you’re following me on Twitter you already know that I spend a great deal of it experimenting and building stuff!
More recently I’ve been doing a bunch of “prompt shares” or “prompt challenges” where folks share their prompts or challenge others to come up with thematic outcomes.
Let’s jump into it, shall we?
For starters, Microsoft released a “Future of Work” report for 2023 and it’s one of the worst designed presentations I’ve seen (but on-point for who they are). It’s incredibly heavy with text and you can guess a lot of the high-level since we’ve already talked about how much money is moving into the space and how many companies, especially the larger enterprises — another reason to get “in it” and get started (read and share this primer if you need a lift).
You can download the .pdf here and I’ve called out a few more tidbits that you might find interesting:
The first point seems obvious to those who are already using generative AI but I do remember that when I first started entertaining the thought I wasn’t so sure that this new form of technology could substantially improve my productivity.
I was wrong.
Even for this simple newsletter I’ve experienced significant time and money savings on just creating unique images for posts. That alone is worth the investment of getting up-to-speed with this stuff! Besides, increased productivity allows me to spend time in harder, more mentally-taxing tasks like writing content (and I do type out my posts and do not use ChatGPT for them).
Continuing forward into Slide 8 they share that LLMs help accelerate new or “low-skilled” persons the most, which is where we all start anyway. Get those gains!
In addition, simply using some of these tools can help with critical thinking and creativity. I’ve also seen this in my own life as a few prompts can help the brain kickstart me in the right direction.
I don’t think GenAI will replace the creative process entirely, of course, but sometimes I just need a starting point. In slide 10 it can help with micro-productivity tasks too, like working out as an example:
John enters into his task list:
Exercise more frequently
TaskGenies responds with the action plan:
Find a workout buddy to keep you accountable
Get a gym membership
Create a weekly exercise schedule
Start working out this Monday and stick to the schedule
Generative AI works best when it’s paired with practical use, which is what this newsletter is all about!
I’ve already mentioned this important point but it bears repeating: The best way to learn GenAI is to simply start. And if you do then you’ll get better over time.
At first it’s a bit awkward and clunky but like most skills the refinement comes out of repetition and iteration. If we’re headed toward a GenAI world then it’s probably best that you get on this train.
As an educator myself I’m really excited about the increased use of great tools to help folks level-up their skills and their lives — there are obvious and clear benefits to LLMs in education (and it’s not about anti-cheating tools — ugh) and making humanity more equipped is a benefit that accrues to all of us.
There are a lot of startups and enterprise companies that have tapped into LLMs as a way of organizing data and there’s a lot of money saved and made as a result of this important work.
Now I’ve worked in large and small companies and there is an unbelievable amount of repeated effort when it comes to socializing important data but there aren’t very many tools that do this effectively. Leadership knows this but oftentimes aren’t willing or able to do it, beyond just time and resources. LLM’s can solve a lot of this pain and maximize staff productivity as a consequence.
Finally, this last slide is hopeful:
Our call is to help move this transformation forward:
We are all going through a period of rapid learning and growth. Fortunately, there’s a model for that: Science. Leaders can take insight from the scientific process.
This means developing a hypothesis and metrics, then doing the experimentation to test the hypothesis.
It also means learning from existing knowledge. While LLMs appear very new, as demonstrated in this report there is great deal that is already know about them. We must build on the state-of-the-art to keep pushing forward.
Sharing what we learn gives others something to build on and creates the opportunity to validate results. We must be open to debate about the best way forward.
Science can also help us consider the externalities we create as we develop new norms, embed new tools, and change how we work.
The 4th point makes my heart warm as I’m doing my part to share what I know and what I’ve learned with y’all! I’m so glad your here.
It’s your turn friends. Let’s get to it! Let me know what your thoughts on this report as well as I’d love any insights that you might have gleaned!
As I promised, here a few of my favorite outputs from the prompt shares / challenges of the past weekend. Lmk what you think!