Artificial intelligencehas become a buzzword in the tech industry.
What are the effects of the current hype surrounding AI?
Is it just misleading consumers and end-users or is it also affecting investors and regulators?
How is it shaping the mindset for creating products and services?
How is the merging of scientific research and commercial product development feeding into the hype?
And by extension, we draw the wrong conclusions and make the wrong decisions.
And this is why being AI-first means doing AI last.
One of the themes that Heimann returns to in the book is having the wrong focus.
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While these models are truly impressive, they are not a silver bullet.
We interpret AI-first as though we ought to literally become solution-first without knowing why.
This is a pain point that Ive encountered time and again in how companies try topitch their products.
Sometimes, I find nothing impressive.
Perhaps these wannapreneurs are looking for a strategic acquisition.
If your dream is to be acquired by Google, you dont always need a business.
Google is one and doesnt need yours.
However, the fact that Google is a business should not be overlooked.
But it has also had adverse effects.
It can also drive companies into overlooking more affordable solutions and waste resources on unnecessary technology.
What is important to remember is that AI is not some monolith.
It means different things to different people, Heimann said.
It cannot be said without confusing everyone.
If youre a manager and say AI, you have created external goals for problem-solvers.
Academic AI research is focused on pushing the boundaries of science.
Scientists study cognition, brain, and behavior in animals and humans to find hints about creating artificial intelligence.
Applied AI, on the other hand, aims to solve specific problems and ship products to the market.
Developers of applied AI systems must meet memory and computational constraints imposed by the environment.
They must conform to regulations and meet safety and robustness standards.
Consequently, their commercial goals affect the directions that AI research takes.
An example is DeepMind, the UK-based AI research lab that was acquired by Google in 2014.
DeepMinds mission is to create safe artificial general intelligence.
At the same time, it has a duty toturn in profits for its owner.
The same can be said of OpenAI, another research lab that chases the dream of AGI.
You also find academics who maintain their positions while holding industry roles.
Academics make inflated claims and create AI-only businesses that solve no problem to grab cash during AI summers.
Companies make big claims with academic support.
This supports human resource pipelines, generally company prestige, and impacts the multiplier effect.
Time and again, scientists have discovered that solutions to many problems dont necessarily require human-level intelligence.
These findings often create debates around whether AI should simulate the human brain or aim at producing acceptable results.
The question is relevant because AI doesnt solve problems in the same way as humans, Heimann said.
This has important implications for safety, security, fairness, trustworthiness, and many other social issues.
Again, this brings us back to the nature of the problem we want to solve.
If insiders generally dont care about bridging the gap between biological and artificial neural networks, neither should you.
If that is the question, then the answer is no.
The presumption that you gotta use some arbitrary solution before you identify a problem is solution guessing.
When it comes to developing products and business plans, the problem comes first, and the technology follows.
Sometimes, in the context of the problem, highlighting the technology makes sense.
(It is worth noting that those two terms also became meaningless buzzwords after being overused.
Today, every utility is expected to be available on mobile and to have a strong cloud infrastructure.)
But what does AI-first say about the problem and context of the app and the problem it solves?
AI-first is an oxymoron and an ego trip.
You cannot do something before you understand the circumstances that make it necessary, Heimann said.
AI strategies, such as AI-first, could mean anything.
Business strategy is too broad when it includes everything or things it shouldnt, like intelligence.
Circular strategies are those in which a solution defines a goal, and the goal defines that solution.
Nevertheless, you are unlikely to find a customer inside an abstract solution like AI.
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